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Abstract Background Innate immune responses can be activated by pathogen-associated molecular patterns (PAMPs), danger signals released by damaged tissues, or the absence of self-molecules that inhibit immunity. As PAMPs are typically conserved across broad groups of pathogens but absent from the host, it is unclear whether they allow hosts to recognize parasites that are phylogenetically similar to themselves, such as parasitoid wasps infecting insects. Results Parasitoids must penetrate the cuticle of Drosophila larvae to inject their eggs. In line with previous results, we found that the danger signal of wounding triggers the differentiation of specialized immune cells called lamellocytes. However, using oil droplets to mimic infection by a parasitoid wasp egg, we found that this does not activate the melanization response. This aspect of the immune response also requires exposure to parasite molecules. The unidentified factor enhances the transcriptional response in hemocytes and induces a specific response in the fat body. Conclusions We conclude that a combination of danger signals and the recognition of nonself molecules is required to activate Drosophila’s immune response against parasitic insects.

Abstract Background Evidence-based care for acute myocardial infarction (AMI) reduces morbidity and mortality. Prior studies in Tanzania identified substantial gaps in the uptake of evidence-based AMI care. Implementation science has been used to improve uptake of evidence-based AMI care in high-income settings, but interventions to improve quality of AMI care have not been studied in sub-Saharan Africa. Methods Purposive sampling was used to recruit participants from key stakeholder groups (patients, providers, and healthcare administrators) in northern Tanzania. Semi-structured in-depth interviews were conducted using a guide informed by the Consolidated Framework for Implementation Research (CFIR). Interview transcripts were coded to identify barriers to AMI care, using the 39 CFIR constructs. Barriers relevant to emergency department (ED) AMI care were retained, and the Expert Recommendations for Implementing Change (ERIC) tool was used to match barriers with Level 1 recommendations for targeted implementation strategies. Results Thirty key stakeholders, including 10 patients, 10 providers, and 10 healthcare administrators were enrolled. Thematic analysis identified 11 barriers to ED-based AMI care: complexity of AMI care, cost of high-quality AMI care, local hospital culture, insufficient diagnostic and therapeutic resources, inadequate provider training, limited patient knowledge of AMI, need for formal implementation leaders, need for dedicated champions, failure to provide high-quality care, poor provider-patient communication, and inefficient ED systems. Seven of these barriers had 5 strong ERIC recommendations: access new funding, identify and prepare champions, conduct educational meetings, develop educational materials, and distribute educational materials. Conclusions Multiple barriers across several domains limit the uptake of evidence-based AMI care in northern Tanzania. The CFIR-ERIC mapping approach identified several targeted implementation strategies for addressing these barriers. A multi-component intervention is planned to improve uptake of evidence-based AMI care in Tanzania.

Abstract Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium. In this study, we replicated the results of our previous study using network propagation methods in a genome-wide association study of an independent sample of 2039 patients from the International Consortium on Lithium Genetics (ConLiGen) study. We identified functional enrichment in focal adhesion and PI3K-Akt pathways, but we did not find an association with the ECM pathway. Our results suggest that deficits in the neuronal growth cone and PI3K-Akt signaling, but not in ECM proteins, may influence response to lithium in BD.

There is an increasing need to improve the accuracy of extreme weather forecasts for life-saving applications and in support of various socioeconomic sectors in East Africa, a region with remarkable mesoscale systems due to its complex topography defined by sharp gradients in elevation, inland water bodies, and landuse conversions. This study sought to investigate the impacts of the Weather Research and Forecasting (WRF) model spatial resolution on resolving rainfall variability with topography utilizing nested domains at 12 and 2.4 km resolutions. The model was driven by the National Centers for Environmental Prediction (NCEP)-Global Data Assimilation System (GDAS) Global Forecast System (GFS) final (FNL) reanalysis to simulate the weather patterns over East Africa from 3rd April 2018 to 30th April 2018, which were evaluated against several freely available gridded weather datasets alongside rainfall data from the Kenya Meteorological Department (KMD) stations. The reference datasets and the model outputs revealed that the highlands had more rainfall events and higher maximum daily rainfall intensity compared to the surrounding lowlands, attributed to orographic lifting enhancing convection. Rainfall was inversely proportional to altitude from 500 m to 1,100 m above sea level (ASL) for both coarse and fine resolutions. The convection-permitting setup was superior in three aspects: resolving the inverse altitude-rainfall relationship for altitudes beyond 3000 m ASL, simulating heavy rainfall events over the lowlands, and resolution of the diurnal cycle of low-level wind. Although the coarse resolution setup reasonably simulated rainfall over large mountains, only the convection-permitting configuration could accurately resolve rainfall variability over contrasting topographical features. The study notes that high-resolution modeling systems and topography-sensitive bias correction techniques are critical for improving the quality of operational weather forecasts in East Africa.

Abstract Background Major advances over the past decade in molecular ecology are providing access to soil fungal diversity in forest ecosystems worldwide, but the diverse functions and metabolic capabilities of this microbial community remain largely elusive. We conducted a field survey in montane old-growth broadleaved and conifer forests, to investigate the relationship between soil fungal diversity and functional genetic traits. To assess the extent to which variation in community composition was associated with dominant tree species (oak, spruce, and fir) and environmental variations in the old-growth forests in the Jade Dragon Snow Mountain in Yunnan Province, we applied rDNA metabarcoding. We also assessed fungal gene expression in soil using mRNA sequencing and specifically assessed the expression of genes related to organic matter decomposition and nutrient acquisition in ectomycorrhizal and saprotrophic fungi. Results Our taxonomic profiling revealed striking shifts in the composition of the saprotrophic and ectomycorrhizal guilds among the oak-, fir-, and spruce-dominated forests. The core fungal microbiome comprised only ~ 20% of the total OTUs across all soil samples, although the overlap between conifer-associated communities was substantial. In contrast, seasonality and soil layer explained only a small proportion of the variation in community structure. However, despite their highly variable taxonomic composition, fungal guilds exhibited remarkably similar functional traits for growth-related and core metabolic pathways across forest associations, suggesting ecological redundancy. However, we found that the expression profiles of genes related to polysaccharide and protein degradation and nutrient transport notably varied between and within the fungal guilds, suggesting niche adaptation. Conclusions Overall, our metatranscriptomic analyses revealed the functional potential of soil fungal communities in montane old-growth forests, including a suite of specialized genes and taxa involved in organic matter decomposition. By linking genes to ecological traits, this study provides insights into fungal adaptation strategies to biotic and environmental factors, and sheds light on the importance of understanding functional gene expression patterns in predicting ecosystem functioning. Video Abstract

This research study proposes a unique photonic crystal fiber (PCF) consisting of a solid core and a hexa-spiral shape, using tellurite as the background material. The proposed PCF exhibits numerous significant optical guiding properties, such as nonlinearity, dispersion, effective index, effective mode area, etc. Simulated findings from COMSOL Multiphysics v5.5 confirm that the PCF can achieve high nonlinearity and zero-dispersion at 1650 nm. The base material is tellurite, which has a significant nonlinear refractive index (RI) of 5.11 × 10−19 m2 W−1. The resultant wave has a bandwidth of 562.65 nm when pumped at a wavelength of 1650 nm. Additionally, a continuum can be produced by pumping at 1500 and 1550 nm. The designed PCF can generate a broad spectrum of supercontinuum with a pulse duration of 200 fs and 10 kW input power. Here is also performed measurements and inspections on the impact of higher-order Taylor series dispersion coefficients, pulse width, input power, etc. The proposed PCF has many potential applications, including nonlinear optics, high dispersion characteristics, infinitely single-mode fibers, high wattage beam transmission, particle trapping, and sensing. To address these applications, here is used COMSOL Multiphysics to generate data, such as propagation constants, attenuations, effective area, and fields, which were used by Optisystem. To the best of our knowledge, this is the first investigation to verify the performance of our designed PCF in Optisystem, for modeling signal transmission for supercontinuum generation and Raman shift.

Introduction: Emergency conditions cause a significant burden of death and disability, particularly in developing countries. Prehospital and Emergency Medical Services (EMS) are largely nonexistent throughout Tanzania and little is known about the community's barriers to accessing emergency care. The objective of this study was to better understand local community stakeholder perspectives on barriers, facilitators, and potential solutions surrounding emergency care in the Kilimanjaro region through the Three Delays Model framework. Methods: A qualitative assessment of local stakeholders was conducted through semi-structured focus group discussions (FGDs) from February to June 2021 with five separate groups: hospital administrators, emergency hospital workers, police personnel, fire brigade personnel, and community health workers. FGDs were conducted in Kiswahili, audio recorded, and translated to English verbatim. Two research analysts separately coded the first two FGDs using both inductive and deductive thematic analysis. A final codebook was then created to analyze the remaining FGDs. Results: A total of 24 participants were interviewed. Thematic analysis revealed that participants identified significant barriers within the Three Delays Model as well as identified an additional delay centered on community members and first aid provision. Perceived delays in the decision to seek care, the first delay, were financial constraints and the lack of community education on emergency conditions. Limited infrastructure and reduced transportation access were thought to contribute to the second delay. Potential barriers to receiving timely appropriate care, the third delay, included upfront payments required by hospitals and emergency department intake delays. Suggested solutions focused on increasing education and improving communication and infrastructure. Conclusion: The findings outline barriers to accessing emergency care from a stakeholder perspective. These themes can support recommendations for further strengthening of the prehospital and emergency care system. Due to logistical constraints, emergency care workers interviewed were all from one hospital and patients were not included.

Despite consensus by the international community on the need for setting targets for reducing emissions or enhancing removal of greenhouse gases, comprehensive strategies for enumerating smallholders’ roles are yet to be designed and tested. Moreover, although smallholders’ role as net greenhouse gas removers has been speculated, evidence of this fact is necessary. A survey of 380 smallholders involved in maize-farming system in four sub-counties of western Kenya representing heterogeneity in agro-ecological zones and postulated biomass cooking energy access and demand was undertaken. The objectives of the study included to i). determine the smallholders’ net role in GHGs’ removals; ii). assess the presence of differences in smallholders’ emission status for an identical system but in dissimilar agro-ecological zones iii). relate atmospheric GHG removers to households' socio-economic characteristics and iv). enumerate a cost-effective approach of estimating atmospheric greenhouse gases emissions and removal. Greenhouse gas emissions and removal activity data, default emission factors and outcomes of allometric equations based on existing secondary sources were incorporated into the primary database from the survey to derive smallholders’ roles as net GHG removers. Economic models were used to derived the drivers of GHG removal. Most smallholders were net CO2e removers with differences among them and the sampled sub-counties associated with intensity in adoption of the maize-agroforestry system and levels of biomass utilization. Net CO2e removers significantly (p < 0.05) reared more livestock, utilized more fertilizer at both planting and topdressing stages, reported higher maize yields and had planted more trees than those who were net dischargers. Factors influencing the probability (p < 0.05) of smallholder being a net carbon remover included household size, maize yield, land owned and adoption of energy saving cooking stoves. Emission mitigation practices reported were compatible with food security, agricultural commercialization and the welfare enhancing production operations. The approach adopted in enumerating greenhouse gases emission and removal could highly enhance developing countries reporting the National GHG's Inventories and pin-point intervention options.

Hand Gesture Recognition (HGR) using electromyography (EMG) signals is a challenging problem due to the variability and noise in the signals across individuals. This study addresses this challenge by examining the effect of incorporating a post-processing algorithm, which filters the sequence of predictions and removes spurious labels, on the performance of a HGR model based on spectrograms and Convolutional Neural Networks (CNN). The study also compares CNN vs CNN-LSTM to assess the influence of the memory cells on the model. The EMG-EPN-612 dataset, which contains measurements of EMG signals for 5 hand gestures from 612 subjects, was used for training and testing. The results showed that the post-processing algorithm increased the recognition accuracy by 41.86% for the CNN model and 24.77% for the CNN-LSTM model. The inclusion of the memory cells increased accuracy by 3.29%, but at the cost of 53 times more learnables. The CNN-LSTM model with post-processing achieved a mean recognition accuracy of 90.55% (SD=9.45%). These findings suggest new paths for research in HGR architectures beyond the traditional focus on the classification and feature extraction stages. For reproducibility purposes, we made publicly available the source code in Github.

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy.

BackgroundChronic myocardial injury is a condition defined by stably elevated cardiac biomarkers without acute myocardial ischemia. Although studies from high-income countries have reported that chronic myocardial injury predicts adverse prognosis, there are no published data about the condition in sub-Saharan Africa.MethodsBetween November 2020 and January 2023, adult patients with chest pain or shortness of breath were recruited from an emergency department in Moshi, Tanzania. Medical history and point-of-care troponin T (cTnT) assays were obtained from participants; those whose initial and three-hour repeat cTnT values were abnormally elevated but within 11% of each other were defined as having chronic myocardial injury. Mortality was assessed thirty days following enrollment.ResultsOf 568 enrolled participants, 81 (14.3%) had chronic myocardial injury, 73 (12.9%) had acute myocardial injury, and 412 (72.5%) had undetectable cTnT values. Of participants with chronic myocardial injury, the mean (± sd) age was 61.5 (± 17.2) years, and the most common comorbidities were CKD (n = 65, 80%) and hypertension (n = 60, 74%). After adjusting for CKD, thirty-day mortality rates (38% vs. 36%, aOR 1.03, 95% CI: 0.52-2.03, p = 0.931) were similar between participants with chronic myocardial injury and those with acute myocardial injury, but significantly greater (38% vs. 13.6%, aOR 3.63, 95% CI: 1.98-6.65, pConclusionIn Tanzania, chronic myocardial injury is a poor prognostic indicator associated with high risk of short-term mortality. Clinicians practicing in this region should triage patients with stably elevated cTn levels in light of their increased risk.

IntroductionMyocardial Infarction (MI) is a leading cause of death worldwide. In high income countries, quality improvement strategies have played an important role in increasing uptake of evidence-based MI care and improving MI outcomes. The incidence of MI in sub-Saharan Africa is rising, but uptake of evidence-based care in northern Tanzania is low. There are currently no published quality improvement interventions from the region. The objective of this study was to determine provider attitudes towards a planned quality improvement intervention for MI care in northern Tanzania.MethodsThis study was conducted at a zonal referral hospital in northern Tanzania. A 41-question survey, informed by the Theoretical Framework for Acceptability, was developed by an interdisciplinary team from Tanzania and the United States. The survey, which explored provider attitudes towards MI care improvement, was administered to key provider stakeholders (physicians, nurses, and hospital administrators) using convenience sampling.ResultsA total of 140 providers were enrolled, including 82 (58.6%) nurses, 56 (40.0%) physicians, and 2 (1.4%) hospital administrators. Most participants worked in the Emergency Department or inpatient medical ward. Providers were interested in participating in a quality improvement project to improve MI care at their facility, with 139 (99.3%) strongly agreeing or agreeing with this statement. All participants agreed or strongly agreed that improvements were needed to MI care pathways at their facility. Though their facility has an MI care protocol, only 88 (62.9%) providers were aware of it. When asked which intervention would be the single-most effective strategy to improve MI care, the two most common responses were provider training (n = 66, 47.1%) and patient education (n = 41, 29.3%).ConclusionProviders in northern Tanzania reported strongly positive attitudes towards quality improvement interventions for MI care. Locally-tailored interventions to improve MI should include provider training and patient education strategies.

The impact of environmental uncertainty on locomotor adaptation remains unclear. Environmental uncertainty could either aid locomotor adaptation by prompting protective control strategies that stabilize movements to assist learning or impede adaptation by reducing error sensitivity and fostering hesitance to pursue corrective movements. To explore this, we investigated participants' adaptation to a consistent force field after experiencing environmental uncertainty in the form of unpredictable balance perturbations. We compared the performance of this group (Perturbation) to the adaptive performance of a group that did not experience any unpredictable perturbations (Non-Perturbation). Perturbations were delivered using a cable-driven robotic device applying lateral forces to the pelvis. We assessed whole-body center of mass (COM) trajectory (COM signed deviation), anticipatory postural adjustments (COM lateral offset), and first step width. The Perturbation group exhibited larger disruptions in COM trajectory (greater COM signed deviations) than the Non-Perturbation group when first walking in the force field. While the COM signed deviations of both groups decreased towards baseline values, only the Non-Perturbation group returned to baseline levels. The Perturbation groups COM signed deviations remained higher, indicating they failed to fully adapt to the force field before the end. The Perturbation group also did not adapt their COM lateral offset to counter the predictable effects of the force field as the Non-Perturbation group did, and their first step width increased more slowly. Our findings suggest that exposure to unpredictable perturbations impeded future sensorimotor adaptations to consistent perturbations.

Both constitutive and inducible immune mechanisms are employed by hosts for defense against infection. Constitutive immunity allows for a faster response, but it comes with an associated cost that is always present. This trade-off between speed and fitness costs leads to the theoretical prediction that constitutive immunity will be favored where parasite exposure is frequent. We selected populations of Drosophila melanogaster under high parasite pressure from the parasitoid wasp Leptopilina boulardi. With RNA sequencing, we found the evolution of resistance in these populations was associated with them developing constitutively active humoral immunity, mediated by the larval fat body. Furthermore, these evolved populations were also able to induce gene expression in response to infection to a greater level, which indicates an overall more activated humoral immune response to parasitization. The anti-parasitoid immune response also relies on the JAK/STAT signaling pathway being activated in muscles following infection, and this induced response was only seen in populations that had evolved under high parasite pressure. We found that the cytokine Upd3, which induces this JAK/STAT response, is being expressed by immature lamellocytes. Furthermore, these immune cells became constitutively present when populations evolved resistance, potentially explaining why they gained the ability to activate JAK/STAT signaling. Thus, under intense parasitism, populations evolved resistance by increasing both constitutive and induced immune defenses, and there is likely an interplay between these two forms of immunity.

Parasites reduce the fitness of their hosts, and different causes of this damage have fundamentally different consequences for the evolution of immune defences. Damage to the host may result from the parasite directly harming its host, often due to the production of virulence factors that manipulate host physiology. Alternatively, the host may be harmed by the activation of its own immune defences, as these can be energetically demanding or cause self-harm. A well-studied model of the cost of infection is Drosophila melanogaster and its common natural enemy, parasitoid wasps. Infected Drosophila larvae rely on humoral and cellular immune mechanisms to form a capsule around the parasitoid egg and kill it. Infection results in a developmental delay and reduced adult body size. To disentangle the effects of virulence factors and immune defences on these costs, we artificially activated anti-parasitoid immune defences in the absence of virulence factors. Despite immune activation triggering extensive differentiation and proliferation of immune cells together with hyperglycaemia, it did not result in a developmental delay or reduced body size. We conclude that the costs of infection do not result from these aspects of the immune response and may instead result from the parasite directly damaging the host.

A very sensitive multi-channel refractive index-based biosensor with a detection range of 1.26 to 1.36 has been proposed. Titanium oxide (TiO2) was utilized as the dielectric, and gold as the plasmonic material in the sensor. The suggested sensor achieved a maximum sensitivity value of 48,000 nm/RIU and 7220 RIU-1 using the wavelength and amplitude interrogation techniques with a pitch layer variation of 5.55–5.95 μm and a metal layer variation of 30–50 nm. The suggested sensor can detect many compounds with amazing sensitivity, such as glucose, sucrose, ethanol, methanol, human intestinal mucosa, sevoflurane, and bio-chemicals. Additionally, a deep learning model that forecasts six optical properties has been proposed. The hyperparameters of the deep neural network model are tuned extensively to maximize accuracy. Our model had a Mean Squared Error of 0.006 and was significantly faster than conventional techniques. The results show that deep learning can predict optical features in photonic sensors, which can identify specific analytes or biomolecules in a sample without costly and time-consuming simulations. Photonic sensor researchers and engineers may utilize the suggested model on vast datasets and adjust it to new settings. This study may open the door to the use of deep learning-based optimization techniques to enhance biosensors.

Abstract Natural environments can promote well-being through multiple mechanisms. Many studies have investigated relationships between residential green/blue space (GBS) and well-being, fewer explore relationships with actual use of GBS. We used a nationally representative survey, the National Survey for Wales, anonymously linked with spatial GBS data to investigate associations of well-being with both residential GBS and time in nature (N = 7631). Both residential GBS and time spent in nature were associated with subjective well-being. Higher green-ness was associated with lower well-being, counter to hypotheses (predicting the Warwick and Edinburgh Mental Well-Being Scale (WEMWBS): Enhanced vegetation index β = − 1.84, 95% confidence interval (CI) − 3.63, − 0.05) but time spent in nature was associated with higher well-being (four hours a week in nature vs. none β = 3.57, 95% CI 3.02, 4.13). There was no clear association between nearest GBS proximity and well-being. In support of the equigenesis theory, time spent in nature was associated with smaller socioeconomic inequalities in well-being. The difference in WEMWBS (possible range 14–70) between those who did and did not live in material deprivation was 7.7 points for those spending no time in nature, and less at 4.5 points for those spending time in nature up to 1 h per week. Facilitating access and making it easier for people to spend time in nature may be one way to reduce socioeconomic inequalities in well-being.

Background: Diabetic bladder dysfunction (DBD) is driven in part by inflammation which dysregulates prostaglandin release in the bladder. Precise inflammatory mechanisms responsible for such dysregulation have been elusive. Since prostaglandins impact bladder contractility, elucidating these mechanisms may yield potential therapeutic targets for DBD. In female Type 1 diabetic Akita mice, inflammation mediated by the nucleotide-binding domain, leucine-rich–containing family, pyrin domain–containing-3 (NLRP3) inflammasome is responsible for DBD. Here, we utilized female Akita mice crossbred with NLRP3 knock-out mice to determine how NLRP3-driven inflammation impacts prostaglandin release within the bladder and prostaglandin-mediated bladder contractions. Methods: Akita mice were crossbred with NLRP3-⁣/- mice to yield four groups of non-diabetics and diabetics with and without the NLRP3 gene. Females were aged to 30 weeks when Akitas typically exhibit DBD. Urothelia and detrusors were stretched ex vivo to release prostaglandins. Prostaglandin E2 (PGE2) and prostaglandin F2α (PGF2α) were quantified using enzyme linked immunosorbent assays (ELISA). In separate samples, ex vivo contractile force to PGE2 and PGF2α +/– the prostaglandin F (FP) receptor antagonist, AL8810, was measured. FP receptor protein expression was determined via western blotting. Results: Stretch-induced PGE2 release increases in urothelia but decreases in detrusors of diabetics. However, PGE2-mediated bladder contractions are not impacted. Conversely, diabetics show no changes in PGF2α release, but PGF2α-mediated contractions increase significantly. This is likely due to signaling through the FP receptors as FP receptor antagonism prevents this increase and diabetics demonstrate a four-fold increase in FP receptor proteins. Without NLRP3-mediated inflammation, changes in prostaglandin release, contractility, and receptor expression do not occur. Conclusion: NLRP3-dependent inflammation dysregulates prostaglandin release and prostaglandin-mediated bladder contractions in diabetic female Akita mice via FP receptor upregulation.

Tobacco use remains a leading preventable cause of morbidity and mortality, with pharmacotherapy and counseling recognized as effective cessation aids. Yet, the potential role of pharmacists and pharmacy technicians in tobacco cessation services is underutilized. This study explores the integration of such services in community pharmacies, identifying facilitators and barriers to their implementation. A qualitative study was conducted across seven community pharmacies in California that were affiliated with the Community Pharmacy Enhanced Services Network. Participants included 22 pharmacists and 26 pharmacy technicians/clerks who completed tobacco cessation training. Data were collected through semi-structured interviews, focusing on experiences with implementing cessation services. The analysis was guided by Rogers’ Diffusion of Innovations Theory. MAXQDA software was used for data management and thematic analysis. Sixteen pharmacy personnel participated in the study, highlighting key themes around the integration of cessation services. Compatibility with existing workflows, the importance of staff buy-in, and the crucial role of pharmacy technicians emerged as significant facilitators. Challenges included the complexity of billing for services, software limitations for documenting tobacco use and cessation interventions, and gaps in training for handling complex patient cases. Despite these barriers, pharmacies successfully initiated cessation services, with variations in service delivery and follow-up practices. Community pharmacies represent viable settings for delivering tobacco cessation services, with pharmacists and technicians playing pivotal roles. However, systemic changes are needed to address challenges related to billing, documentation, and training. Enhancing the integration of cessation services in community pharmacies could significantly impact public health by increasing access to effective cessation support.

Background: Uptake of evidence-based care for acute myocardial infarction (AMI) is suboptimal in Tanzania, but there are currently no published interventions to improve AMI care in sub-Saharan Africa. Objectives: Co-design a quality improvement intervention for AMI care tailored to local contextual factors. Methods: An interdisciplinary design team consisting of 20 physicians, nurses, implementation scientists, and administrators met from June 2022 through August 2023. Half of the design team consisted of representatives from the target audience, emergency department physicians and nurses at a referral hospital in northern Tanzania. The design team reviewed multiple published quality improvement interventions focusing on ED-based AMI care. After selecting a multicomponent intervention to improve AMI care in Brazil (BRIDGE-ACS), the design team used the ADAPT-ITT framework to adapt the intervention to the local context. Findings: The design team audited current AMI care processes at the study hospital and reviewed qualitative data regarding barriers to care. Multiple adaptations were made to the original BRIDGE-ACS intervention to suit the local context, including re-designing the physician reminder system and adding patient educational materials. Additional feedback was sought from topical experts, including patients with AMI. Draft intervention materials were iteratively refined in response to feedback from experts and the design team. The finalized intervention, Multicomponent Intervention to Improve Myocardial Infarction Care in Tanzania (MIMIC), consisted of five core components: physician reminders, pocket cards, champions, provider training, and patient education. Conclusion: MIMIC is the first locally tailored intervention to improve AMI care in sub-Saharan Africa. Future studies will evaluate implementation outcomes and efficacy.

Objectives We aimed to prospectively describe incident cardiovascular events among people living with HIV (PLWH) in northern Tanzania. Secondary aims of this study were to understand non-communicable disease care-seeking behaviour and patient preferences for cardiovascular care and education.Design A prospective observational study.Setting This study was conducted at the Majengo HIV Care and Treatment Clinic, an outpatient government-funded clinic in Moshi, TanzaniaParticipants Adult patients presenting to an HIV clinic for routine care in northern Tanzania were enrolled from 1 September 2020 to 1 March 2021.Interventions At enrolment, participants completed a survey and a resting 12-lead ECG was obtained. At 6 month follow-up, a repeat survey regarding interim health events and repeat ECG was obtained.Primary and secondary outcome measures Interim major adverse cardiovascular events (MACE) were defined by: self-reported interim stroke, self-reported hospitalisation for heart failure, self-reported interim myocardial infarction, interim myocardial infarction by ECG criteria (new pathologic Q waves in two contiguous leads) or death due to cardiovascular disease (CVD).Results Of 500 enrolled participants, 477 (95.4%) completed 6 month follow-up and 3 (0.6%) died. Over the 6 month follow-up period, 11 MACE occurred (3 strokes, 6 myocardial infarctions, 1 heart failure hospitalisation and 1 cardiovascular death), resulting in an incidence rate of 4.58 MACE per 100 person-years. Of participants completing 6 month follow-up, 31 (6.5%) reported a new non-communicable disease diagnosis, including 23 (4.8%) with a new hypertension diagnosis.Conclusions The incidence of MACE among PLWH in Tanzania is high. These findings are an important preliminary step in understanding the landscape of CVD among PLWH in Tanzania and highlight the need for interventions to reduce cardiovascular risk in this population.

Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. Most existing studies only focus on the accuracy of arrhythmia classification, instead of runtime performance of the workflow. In this paper, we present our work on supporting real-time arrhythmic detection using convolutional neural networks, which take images of electrocardiogram (ECG) segments as input, and classify the arrhythmia conditions. To support real-time processing, we have carried out extensive experiments and evaluated the computational cost of each step of the classification workflow. Our results show that it is feasible to achieve real-time arrhythmic detection using convolutional neural networks. To further demonstrate the generalizability of this approach, we used the trained model with processed data collected by a customized wearable sensor from a lab setting, and the results shown that our approach is highly accurate and efficient. This research provides the potentials to enable in-home real-time heart monitoring based on 2D image data, which opens up opportunities for integrating both machine learning and traditional diagnostic approaches.

Summary: Harnessing the differences between cancer and non-cancer tissues presents new opportunities for selective targeting by anti-cancer drugs. CD13, a heavily glycosylated protein, is one example with significant unmet clinical potential in cancer drug discovery. Despite its high expression and activity in cancers, CD13 is also expressed in many normal tissues. Here, we report differential tissue glycosylation of CD13 across tissues and demonstrate for the first time that the nature and pattern of glycosylation of CD13 in preclinical cancer tissues are distinct compared to normal tissues. We identify cancer-specific O-glycosylation of CD13, which selectively blocks its detection in cancer models but not in normal tissues. In addition, the metabolism activity of cancer-expressed CD13 was observed to be critically dependent on its unique glycosylation. Thus, our data demonstrate the existence of discrete cancer-specific CD13 glycoforms and propose cancer-specific CD13 glycoforms as a clinically useful target for effective cancer-targeted therapy.

Menopause is an obligatory phenomenon in a woman’s life. Some women face mental and physical issues during their menopausal period. Depression is one of the issues some women struggle with during their menopausal period. The scarcity of specialists, lack of knowledge, and awareness is the motivating factor in this research to predict depression among menopausal women and enhance their quality of life. The prediction of depression symptoms among menopausal women with machine learning techniques is promising and challenging in artificial intelligence. This study develops a system with significant accuracy using a supervised machine-learning approach. Various classification algorithms are used to determine the best-performing classifier by evaluating multiple parameters, including accuracy, sensitivity, specificity, precision, recall, F-Measure, Receiver Operating Characteristic (ROC), Precision–Recall​ Curve (PRC), and Area Under the Curve (AUC). We found that Random Forest and XGBoost classifiers are the performers with 99.04% accuracy employing the 14 most significant features.

In this study, we propose machine learning (ML) for risk factors analysis and survival prediction of Heart Failure (HF) patients using a survival dataset. Five supervised ML methods are applied to the dataset: Decision Tree (DT), Decision Tree Regressor (DTR), Random Forest (RF), XGBoost, and Gradient Boosting (GB) algorithms. We compare the applied algorithms’ performances based on accuracy, precision, recall, F-measure, and log loss value and show RF provides the highest accuracy of 97.78%. The analysis of the risk factors shows the most predictive features based on coefficients and feature importance. The top six risk factors for HF patients are serum creatinine (SC), age, ejection fraction (EF), platelets, creatinine phosphokinase (CPK), and SS (SS). Further analysis of these factors shows significant clustering of the features. The survival analysis finds that the increment of SC, age, and SS and the decrement of EF are the most significant risk factors for HF patients. Our results suggest that HF survival prediction is possible with higher accuracy using the proposed model. Our ML models are useful in clinical settings for screening patients with HF probability.

Background Cross-sectional evidence suggests that living near green and blue spaces benefits mental health; longitudinal evidence is limited. Objectives To quantify the impact of changes in green and blue spaces on common mental health disorders, well-being and health service use. Design A retrospective, dynamic longitudinal panel study. Setting Wales, UK. Participants An e-cohort comprising 99,682,902 observations of 2,801,483 adults (≥ 16 years) registered with a general practice in Wales (2008–2019). A 5312-strong ‘National Survey for Wales (NSW) subgroup’ was surveyed on well-being and visits to green and blue spaces. Main outcome measures Common mental health disorders, general practice records; subjective well-being, Warwick–Edinburgh Mental Well-being Scale. Data sources Common mental health disorder and use of general practice services were extracted quarterly from the Welsh Longitudinal General Practice Dataset. Annual ambient greenness exposure, enhanced vegetation index and access to green and blue spaces (2018) from planning and satellite data. Data were linked within the Secure Anonymised Information Linkage Databank. Methods Multilevel regression models examined associations between exposure to green and blue spaces and common mental health disorders and use of general practice. For the National Survey for Wales subgroup, generalised linear models examined associations between exposure to green and blue spaces and subjective well-being and common mental health disorders. Results and conclusions Our longitudinal analyses found no evidence that changes in green and blue spaces through time impacted on common mental health disorders. However, time-aggregated exposure to green and blue spaces contrasting differences between people were associated with subsequent common mental health disorders. Similarly, our cross-sectional findings add to growing evidence that residential green and blue spaces and visits are associated with well-being benefits: Greater ambient greenness (+ 1 enhanced vegetation index) was associated with lower likelihood of subsequently seeking care for a common mental health disorder [adjusted odds ratio (AOR) 0.80, 95% confidence interval, (CI) 0.80 to 0.81] and with well-being with a U-shaped relationship [Warwick–Edinburgh Mental Well-being Scale; enhanced vegetation index beta (adjusted) –10.15, 95% CI –17.13 to −3.17; EVI2 beta (quadratic term; adj.) 12.49, 95% CI 3.02 to 21.97]. Those who used green and blue spaces for leisure reported better well-being, with diminishing extra benefit with increasing time (Warwick–Edinburgh Mental Well-being Scale: time outdoors (hours) beta 0.88, 95% CI 0.53 to 1.24, time outdoors2 beta −0.06, 95% CI −0.11 to −0.01) and had 4% lower odds of seeking help for common mental health disorders (AOR 0.96, 95% CI 0.93 to 0.99). Those in urban areas benefited most from greater access to green and blue spaces (AOR 0.89, 95% CI 0.89 to 0.89). Those in material deprivation benefited most from leisure time outdoors (until approximately four hours per week; Warwick–Edinburgh Mental Well-being Scale: time outdoors × in material deprivation: 1.41, 95% CI 0.39 to 2.43; time outdoors2 × in material deprivation −0.18, 95% CI −0.33 to −0.04) although well-being remained generally lower. Limitations Longitudinal analyses were restricted by high baseline levels and limited temporal variation in ambient greenness in Wales. Changes in access to green and blue spaces could not be captured annually due to technical issues with national-level planning datasets. Future work Further analyses could investigate mental health impacts in population subgroups potentially most sensitive to local changes in access to specific types of green and blue spaces. Deriving green and blue spaces changes from planning data is needed to overcome temporal uncertainties. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (Project number 16/07/07) and will be published in full in Public Health Research; Vol. 11, No. 10. Sarah Rodgers is part-funded by the NIHR Applied Research Collaboration North West Coast. Plain language summary We investigated whether people who live near or visit green (parks, woodlands) and blue (riversides, beaches) spaces have fewer common mental health disorders (anxiety or depression), and better well-being. We considered whether changes in the amount of green and blue space around the home affected people’s mental health. We assessed the availability of local green and blue spaces. Annual exposure and access to local green and blue spaces were extracted from planning and satellite data. We linked these data to anonymised health records of 2,801,483 adults registered with a general practice from 2008 to 2019, and to survey answers about leisure visits to natural environments and well-being. We found: people who lived in greener and bluer areas were less likely to seek help for a common mental health disorder than those in less green or blue areas, with those living in the most deprived areas benefiting the most people who used green and blue spaces for leisure, especially those with the greatest levels of deprivation, had better well-being and were less likely to seek help for common mental health disorders no evidence that changing amounts of green and blue space affected how likely people were to seek help for common mental health disorders; this may be because we found mostly small changes in green and blue space, and we may not have allowed enough time between moving home and recording mental health. We found evidence for relationships between green and blue space and mental health. However, some analyses were restricted due to lack of data on changes in green and blue spaces. An important finding was that people in deprived communities appear to benefit the most. Provision of green and blue spaces could be a strategy to improve the mental health of people living in disadvantaged areas. Scientific summary Background Cross-sectional evidence suggests that living close to and spending time in and around green and blue spaces (GBS, e.g. parks, gardens, ponds, lakes, rivers) is associated with higher levels of well-being and fewer mental health problems. Some of these benefits could result from living near GBS; others (e.g. physical activity) are attributed to using GBS. There are few longitudinal studies examining whether changes in GBS affect subsequent mental health. Existing studies have largely had small cohorts, assessed change over a short time, did not investigate socioeconomic inequalities in effects and neglected to consider that some health benefits depend on using GBS rather than simple exposure. Objectives We examined the research questions: Is a greater residential exposure and access to GBS associated with the likelihood of seeking help for a common mental health disorder (CMD) in general practice? Is the association between exposure, or access to, GBS and seeking help for a CMD modified by socioeconomic disadvantage and moving home? Is a greater residential exposure associated with the likelihood of using general practice services? Is greater residential GBS exposure and access to GBS associated with higher subjective well-being or a lower likelihood of seeking help for CMD, and does intentional use of GBS for leisure mediate these associations? Are associations between residential GBS exposure and access to GBS and use and subjective well-being mediated by walking-specific physical activity and modified by socioeconomic disadvantage? Methods We used a dynamic longitudinal panel study design to retrospectively examine natural and non-random variation in GBS exposure during an 11-year period, with individual linkage to survey responses. Data sources We constructed an electronic cohort of the population of Wales using data from the Welsh Demographic Service Dataset. Household-level ambient greenness was determined from satellite imagery, access to GBS from planning records, CMD and service use from Welsh Longitudinal General Practice Dataset (WLGPD) and for a subgroup, well-being and behaviour responses to the National Survey for Wales (NSW, 2016/17 and 2018/19). Data are held in the Secure Anonymised Information Linkage (SAIL) databank. Inclusion criteria The cohort included everyone aged 16 years and over registered with a general practitioner (GP) providing patient records to the SAIL databank between January 2008 and October 2019 (‘the study period’). Those not registered with an appropriate GP, not having a Welsh residential address between January 2008 and October 2019, and not having sex/week of birth recorded were excluded from the study sample. Exposures Our primary environmental exposure was ambient greenness, measured yearly (2008–19) by mean enhanced vegetation index (EVI) averaged over a 300 m circular buffer centred on each residence. Our secondary environmental exposure was the potential for an individual to access GBS along paths/roads within 1600 m of each home, modelled for 2018. We captured changes only when individuals moved home. For research questions 1–3, GBS access was quantified by the number of potentially accessible GBS. For research questions 4–5, access was quantified as the proximity to the nearest GBS. Outcome measures For research questions 1–3, our primary outcome was seeking help for CMD in general practice, identified from practice records using a validated algorithm. Our secondary outcome was the quarterly count of general practice events. For research questions 4 and 5, our primary outcome was subjective well-being, measured by the Warwick–Edinburgh Mental Well-being Scale (WEMWBS) in the NSW. Secondary outcomes were life satisfaction (NSW) and seeking help for CMD in general practice. Self-reported time outdoors on recreational visits to GBS in Wales and walking-specific physical activity were identified (NSW). Statistical analysis Research questions 1–3 were addressed using complementary approaches: We first evaluated associations between cumulative EVI exposure and seeking help for CMD in general practice using multivariate logistic regression. Follow-up stopped when help was first sought for CMD or at the end of the study period. This time aggregated design allowed us to consider environmental exposure prior to seeking help for CMD in general practice and included differences between people. We conducted stratified analyses according to area-level deprivation (Welsh Index of Multiple Deprivation, WIMD) quintiles, home moves (none/one/more than one), history of seeking care for CMD in general practice (before January 2008) and urban-rural home location. Next, we assessed associations between longitudinal change in GBS exposures for the same individuals, and their subsequent likelihood of seeking help for CMD (primary outcome) using multivariate logistic regression. For the association with general practice services use (secondary outcome) we used Poisson regression. We used a panel design with a multilevel model structure (quarterly observations nested within individuals). We accounted for clustering of observations over time (using random effects), and unbalanced data (number of observations varying for individuals). Change in GBS exposure was modelled as a continuous variable relative to previous time periods. For people who did not move home, we modelled EVI change categories to examine in situ exposure changes. Stratified analyses were conducted for quintiles of WIMD and number of home moves. For research questions 4–5 we assessed whether GBS exposures were related to WEMWBS, life satisfaction and seeking help for CMD, and whether there was moderation by socioeconomic disadvantage. We examined whether the association between GBS exposures and WEMWBS, life satisfaction and seeking help for CMD, were mediated by time spent outdoors. Generalised additive models were used to identify any non-linear associations and inform subsequent generalised linear regression. When predicting WEMWBS, linear and quadratic terms for EVI were included and proximity to nearest GBS was categorised. For seeking help for CMD, EVI was categorised, and proximity was continuous. To assess moderation by socioeconomic disadvantage, we included interactions between material deprivation and the GBS exposures. Results The cohort included 2,801,483 individuals (99,682,902 observations). A total of 816,242 individuals (29.1%) sought help for CMD from their general practice at least once in the study period. Research question 1: Is a greater residential exposure and access to GBS associated with the likelihood of seeking health for CMDs in general practice? People who lived in homes surrounded by more ambient greenness (+ 0.1 EVI) were associated with 20% lower odds of seeking help for CMD in general practice [adjusted odds ratio (AOR) 0.80, 95% confidence interval (CI) 0.80 to 0.81]. More potentially accessible GBS (equivalent to 236 more GBS) were associated with 7% lower odds of seeking help for CMD (AOR 0.93, 95% CI 0.93 to 0.93). Every additional 360 m (0.1 unit) to the nearest GBS was associated with 5% higher odds of seeking help for CMD (AOR 1.05, 95% CI 1.04 to 1.05). People experiencing an increase of 0.1 mean EVI was not associated with seeking help for CMD in general practice (exposure change relative to: previous quarter/year/baseline AOR 1.00, 95% CI 1.00 to 1.00). Among the 1,611,581 people who did not move home (57.5% of the cohort), increases or decreases of ≥ 0.15 EVI relative to baseline were not associated with seeking help for CMD (AOR for ≥ +0.15 EVI 1.00, 95% CI 0.99 to 1.00; AOR for ≥ −0.15 EVI 1.00, 95% CI 1.00 to 1.01). Among those who moved home at least once, an increase in the number of potentially accessible GBS was not associated with seeking help for CMD in general practice (GBS increase relative to previous quarter/year/baseline: AOR 1.00, 95% CI 1.00 to 1.00). Research question 2: Is the association between exposure, or access to, GBS and seeking help for a CMD modified by socioeconomic disadvantage and moving home? Living in a home with greater EVI (+ 0.1) and more accessible GBS (+ 236) was associated with lower odds of seeking help for CMD in general practice in all deprivation quintiles and whether people moved. The odds of seeking help were lower for those who did not move (AOR non-movers 0.77, 95% CI 0.77 to 0.78; AOR moved > once 0.92, 95% CI 0.91 to 0.93). There was no evidence that EVI or GBS changes were associated with seeking help for a CMD for people living in either the most or least deprived urban areas. An increase of 0.1 mean EVI (relative to baseline) was associated with slightly lower odds of seeking help for CMD for people who moved home more than once (AOR 0.99, 95% CI 0.99 to 0.99) but not for those who moved once or had not moved. Association between different GBS exposures and seeking help for CMD in general practice were modified by urban–rural home location and a history of seeking help for CMD. Greater EVI was associated with lower odds of seeking help for a CMD for those living in rural areas (AOR 0.59, 95% CI 0.58 to 0.59) compared with urban areas (AOR 0.85, 95% CI 0.84 to 0.85). More potentially accessible GBS were only associated with lower odds of seeking help for CMD for those living in urban areas (AOR 0.89, 95% CI 0.89 to 0.89). In rural areas more GBS access was associated with higher odds of seeking help for CMD (AOR 1.10, 95% CI 1.08 to 1.12). More ambient greenness (+ 0.1 EVI) was associated with lower odds of seeking help for a CMD for those who had historically sought help for CMD (before January 2008) than people who had not (AOR 0.68 vs. 0.84). Research question 3: Is a greater residential exposure associated with the likelihood of using GP services? There was no evidence that an increase in mean EVI was associated with the number of general practice event days/quarter (adjusted incidence rate ratio relative to baseline: 1.00, 95% CI 1.00 to 1.00). Research question 4: Is greater residential GBS exposure and access to GBS associated with higher subjective well-being, or a lower likelihood of seeking help for CMD in general practice, and are these associations mediated by intentional use of GBS for leisure? Among NSW respondents with outcome measures (n = 5971) EVI was significantly related to well-being as measured by WEMWBS with a U-shaped relationship (linear regression; EVI −10.15, 95% CI −17.13 to −3.17; EVI2 12.49, 95% CI 3.02 to 21.97) and weekly time outdoors (linear relationship; EVI 43.41, 95% CI 6.85 to 79.97). Time outdoors was significantly positively related to WEMWBS [time outdoors (hours) 0.88, 95% CI 0.53 to 1.24, time outdoors (hours)2 −0.06, 95% CI −0.11 to −0.01] and life satisfaction [linear relationship; time outdoors (hours) 0.06, 95% CI 0.04 to 0.07]. There was no evidence that time outdoors mediated the relationship between EVI and WEMWBS/life satisfaction. EVI was not associated with CMD (EVI 0.4 to 0.8 vs.

BackgroundIndividual responses to behavioural weight loss interventions can vary significantly, and a better understanding of the factors associated with successful treatment might help to target interventions for those who will benefit the most. We sought to identify demographic and clinical characteristics that predicted intervention “success” (defined as ≥5% weight loss) and other health gains in patients with severe obesity attending a ten-week structured lifestyle modification programme.MethodsWe conducted a prospective cohort study of all 1122 patients (751 (66.9%) female, mean age 47.3 ± 11.9 years, mean body mass index (BMI) 46.7 ± 7.8 kgm-2) referred from our hospital-based obesity clinic, who started the structured lifestyle programme between 2012-2019. We compared routine clinical measures such as weight, fitness, blood pressure, lipids and HbA1c at baseline and follow-up. We also used validated questionnaires to quantify anxiety, depression and health-related quality of life.ResultsOf 1122 patients who started, 877 (78.2%) completed the programme and attended for follow up. Of these, 12.8% lost ≥5% body weight. The amount of weight lost was a strong and consistent predictor of improvements in metabolic, cardiovascular, and mental health, even after adjusting for age, sex, programme attendance and baseline fitness. Older age, male sex, being physically active and having lower anxiety and depression scores at baseline predicted greater weight loss. Younger age, depression and longer wait time to start the intervention were associated with drop-out.ConclusionsIn adults with severe obesity completing a structured lifestyle modification programme, older age and good mental health were associated with programme completion and attaining ≥5% weight loss. The magnitude of weight lost was a strong predictor of improvements in cardiovascular, metabolic and mental health associated with programme completion.

Summary: Background: Living in greener areas, or close to green and blue spaces (GBS; eg, parks, lakes, or beaches), is associated with better mental health, but longitudinal evidence when GBS exposures precede outcomes is less available. We aimed to analyse the effect of living in or moving to areas with more green space or better access to GBS on subsequent adult mental health over time, while explicitly considering health inequalities. Methods: A cohort of the people in Wales, UK (≥16 years; n=2 341 591) was constructed from electronic health record data sources from Jan 1, 2008 to Oct 31, 2019, comprising 19 141 896 person-years of follow-up. Household ambient greenness (Enhanced Vegetation Index [EVI]), access to GBS (counts, distance to nearest), and common mental health disorders (CMD, based on a validated algorithm combining current diagnoses or symptoms of anxiety or depression [treated or untreated in the preceding 1-year period], or treatment of historical diagnoses from before the current cohort [up to 8 years previously, to 2000], where diagnosis preceded treatment) were record-linked. Cumulative exposure values were created for each adult, censoring for CMD, migration out of Wales, death, or end of cohort. Exposure and CMD associations were evaluated using multivariate logistic regression, stratified by area-level deprivation. Findings: After adjustment, exposure to greater ambient greenness over time (+0·1 increased EVI on a 0–1 scale) was associated with lower odds of subsequent CMD (adjusted odds ratio 0·80, 95% CI 0·80–0·81), where CMD was based on a combination of current diagnoses or symptoms (treated or untreated in the preceding 1-year period), or treatments. Ten percentile points more access to GBS was associated with lower odds of a later CMD (0·93, 0·93–0·93). Every additional 360 m to the nearest GBS was associated with higher odds of CMD (1·05, 1·04–1·05). We found that positive effects of GBS on mental health appeared to be greater in more deprived quintiles. Interpretation: Ambient exposure is associated with the greatest reduced risk of CMD, particularly for those who live in deprived communities. These findings support authorities responsible for GBS, who are attempting to engage planners and policy makers, to ensure GBS meets residents' needs. Funding: National Institute for Health and Care Research Public Health Research programme.

Predicting fetal brain abnormalities (FBAs) is an urgent global problem, as nearly three of every thousand women are pregnant with neurological abnormalities. Therefore, early detection of FBAs using deep learning (DL) can help to enhance the planning and quality of diagnosis and treatment for pregnant women. Most of the research papers focused on brain abnormalities of newborns and premature infants, but fewer studies concentrated on fetuses. This study proposed a deep learning-CNN-based framework named StackFBAs that utilized the stacking strategy to classify fetus brain abnormalities more accurately using MRI images at an early stage. We considered the Greedy-based Neural architecture search (NAS) method to identify the best CNN architectures to solve this problem utilizing brain MRI images. A total of 94 CNN architectures were generated from the NAS method, and the best 5 CNN models were selected to build the baseline models. Subsequently, the probabilistic scores of these baseline models were combined to construct the final meta-model (KNN) utilizing the stacking strategy. The experimental results demonstrated that StackFBAs outperform pre-trained CNN Models (e.g., VGG16, VGG19, ResNet50, DenseNet121, and ResNet152) with transfer learning (TL) and existing models with the 5-fold cross-validation tests. StackFBAs achieved an overall accuracy of 80%, an F1-score of 78%, 76% sensitivity, and a specificity of 78%. Moreover, we employed the federated learning technique that protects sensitive fetal MRI data, combines results, and finds common patterns from many users, making the model more robust for the privacy and security of user-sensitive data. We believe that our novel framework could be used as a helpful tool for detecting brain abnormalities at an early stage.

Abstract Wolbachia is a common endosymbiont that can protect insects against viral pathogens. However, whether the antiviral effects of Wolbachia have a significant effect on fitness remains unclear. We have investigated the interaction between Drosophila melanogaster, Wolbachia and two viruses that we recently isolated from wild flies, La Jolla virus (LJV; Iflaviridae) and Newfield virus (NFV; Permutotetraviridae). Flies infected with these viruses have increased mortality rates, and NFV partially sterilizes females. These effects on fitness were reduced in Wolbachia-infected flies, and this was associated with reduced viral titres. However, Wolbachia alone also reduces survival, and under our experimental conditions these costs of the symbiont can outweigh the benefits of antiviral protection. In contrast, protection against the sterilizing effect of NFV leads to a net benefit of Wolbachia infection after exposure to the virus. These results support the hypothesis that Wolbachia is an important defense against the natural pathogens of D. melanogaster. Furthermore, by reducing the cost of Wolbachia infection, the antiviral effects of Wolbachia may aid its invasion into populations and help explain why it is so common in nature.

In this paper, a biosensing absorber based on phase transition material is presented. Different phases of the Ge2Sb2Te5 (GST) substrate have been studied for the suggested absorber with controllable characteristics. The structure has been examined to determine the infrared absorption characteristics. The detection of varying volumes of hemoglobin and urine biomolecules is studied. The graphene-GST material is utilized for spectrum tuning. The tuning for two distinct phases of GST material, amorphous GST and crystalline GST is examined. The results for aGST and cGST are reported in the form of absorption. Different amounts of hemoglobin and urine biomolecules are used to tune these two GST stages. Based on the wavelength shifts at these various concentrations, the sensitivity is computed. The highest achievable sensitivity for hemoglobin and urine biomolecules is 1500 nm/RIU and 1667 nm/RIU. The developed model is observed for various geometrical parameters and incidence angles, from which it is determined that the suggested structure is insensitive to wide angles between 0° and 60°. For urine biomolecules, the aGST design is more sensitive than the cGST design, but similar results are achieved for hemoglobin biomolecules. Experiments are conducted with Machine Learning-based regression models to minimize the simulation time and resource requirements of biosensor design. The findings of the trials indicate that a regression model can accurately estimate the absorption values for intermediate wavelengths with an R2 score of 0.9999.

Abstract The primary purpose of this study was to assess the translatability of preclinical to early clinical tolerable and pharmacologically active dose ranges for central nervous system (CNS) active drugs. As a part of this, IBs were reviewed on reporting quality. Investigator’s Brochures (IBs) of studies performed at the Centre for Human Drug Research (CHDR) reporting statistically significant results of CNS activity related to the drug’s mechanism of action were included. The quality of IBs was assessed based on the presence of a rationale for the chosen animal model, completeness of pharmacokinetic (PK) results in reporting and internal validity information of the preclinical evidence. The IB-derisk tool was used to generate preclinical and early clinical data overviews data. For each compound, the overlap between pharmacologically active dose ranges and well-tolerated levels was calculated for three pharmacokinetic (PK) parameters: human equivalent dose (HED), maximum plasma concentration (C max) and area under the curve (AUC). Twenty-five IBs were included. In general, the quality of reporting in IBs was assessed as poor. About a third of studies did not explore the entire concentration-effect curve (pre)clinically. Single dose tolerability ranges were most accurately predicted by C max. Human equivalent dose and AUC were the best predictors of pharmacologically active ranges. Tolerable and pharmacologically active dose ranges in healthy volunteers can be reasonably well predicted from preclinical data with the IB-derisk tool. The translatability of preclinical studies can be improved by applying a higher reporting standard in IBs including comparable PK measurements across all preclinical and clinical studies.

Primitive meteorite groups such as the Vigarano, Mighei, and Karoonda carbonaceous chondrites have enigmatic patterns of elemental abundances, with moderately volatile elements—those that transition from vapor to condensate between ∼400 and ∼900 K—defining plateaus of subequal abundances despite a wide range in volatility. In detail, each group defines a plateau with distinctive nonmonotonic “chemical fingerprints” that have been attributed to combinations of mixing, vaporization/condensation, and fluid-mediated metasomatism—but the extent to which these processes can reproduce the observed variability has not been quantified. Starting with primitive Ivuna chondrite, a two-stage, two-component equilibrium condensation–vaporization model—with gravity implemented as Jeans escape—can explain large-scale plateaus in these chondrite groups, as well as more complex, nonmonotonic small-scale variations. For all three chondritic meteorite groups, models favor earlier high-temperature fractionation under low-gravity conditions followed by a low-temperature fractionation event that took place on a protoplanet at least as large as Ceres. The second fractionation event may represent the fractionation of incoming materials to the planetesimal during protracted pebble accretion. Models with only thermally driven volatile loss, gravity, and mixing can explain more than 80% of the observed compositional variability in these meteorite groups. In our five-parameter model, using only five randomly selected elements yields uselessly large ranges of planet sizes and temperatures, ranges that converge with increasing numbers of elements. These results suggest that even simple models are prone to generating inaccurate conclusions when constrained by too few observations, a fault likely held by more complex models as well.

Abstract Background In today’s society, cancer has become a big concern. The most common cancers in women are breast cancer (BC), endometrial cancer (EC), ovarian cancer (OC), and cervical cancer (CC). CC is a type of cervix cancer that is the fourth most common cancer in women and the fourth major cause of death. Results This research uses a network approach to discover genetic connections, functional enrichment, pathways analysis, microRNAs transcription factors (miRNA-TF) co-regulatory network, gene-disease associations, and therapeutic targets for CC. Three datasets from the NCBI’s GEO collection were considered for this investigation. Then, using a comparison approach between the datasets, 315 common DEGs were discovered. The PPI network was built using a variety of combinatorial statistical approaches and bioinformatics tools, and the PPI network was then utilized to identify hub genes and critical modules. Conclusion Furthermore, we discovered that CC has specific similar links with the progression of different tumors using Gene Ontology terminology and pathway analysis. Transcription factors-gene linkages, gene-disease correlations, and the miRNA-TF co-regulatory network were revealed to have functional enrichments. We believe the candidate drugs identified in this study could be effective for advanced CC treatment.

Abstract In this large cohort of healthcare workers, we aimed to estimate the rate of reinfections by SARS-CoV-2 over 2 years of the COVID-19 pandemic. We investigated the proportion of reinfections among all the cases of SARS-CoV-2 infection from March 10, 2020 until March 10, 2022. Reinfection was defined as the appearance of new symptoms that on medical evaluation were suggestive of COVID-19 and confirmed by a positive RT-PCR. Symptoms had to occur more than 90 days after the previous infection. These 2 years were divided into time periods based on the different variants of concern (VOC) in the city of São Paulo. There were 37,729 medical consultations due to COVID-19 at the hospital’s Health Workers Services; and 25,750 RT-PCR assays were performed, of which 23% (n = 5865) were positive. Reinfection by SARS-CoV-2 was identified in 5% (n = 284) of symptomatic cases. Most cases of reinfection occurred during the Omicron period (n = 251; 88%), representing a significant increase on the SARS-CoV-2 reinfection rate before and during the Omicron variant period (0.8% vs. 4.3%; p

Summary: Peripheral arterial disease (PAD) is one of the leading causes of cardiovascular morbidity and mortality worldwide, yet current trials on therapeutic angiogenesis remain suboptimal. Type 2 immunity is critical for post-ischemic regeneration, but its regulatory role in revascularization is poorly characterized. Here, we show that type 2 cytokines, interleukin-4 (IL-4) and interleukin-13 (IL-13), are the key mediators in post-ischemic angiogenesis. IL-4/IL-13-deficient mice exhibit impaired reperfusion and muscle repair in an experimental model of PAD. We find that deletion of IL-4Rα in the endothelial compartment, rather than the myeloid compartment, leads to remarkable impairment in revascularization. Mechanistically, IL-4/IL-13 promote endothelial cell proliferation, migration, and tube formation via IL-4Rα/STAT6 signaling. Furthermore, attenuated IL-4/IL-13 expression is associated with the angiogenesis deficit in the setting of diabetic PAD, while IL-4/IL-13 treatment rescues this defective regeneration. Our findings reveal the therapeutic potential of type 2 cytokines in treating patients with muscle ischemia.

Abstract Background Cystic fibrosis (CF) is the most common autosomal recessive condition in Caucasian populations globally. To date, there has been very little research conducted into the oral health of adults with CF possibly due to historic premature mortality. The purpose of this survey was to ascertain knowledge, attitude and practices among dental professionals regarding the oral health of people with cystic fibrosis (PWCF) as part of a larger study being conducted in Ireland. Methods A cross‐sectional survey of dental practitioners in Ireland was conducted via an online questionnaire. The survey contained close‐ended questions, and clinical scenarios which allowed for both close‐ended and free text responses in relation to the provision of dental treatment for PWCF. Data was subject to descriptive data analysis using IBM SPSS 29. Results The results from the survey indicate a wide variety in the knowledge, attitudes and practices of dental practitioners regarding the oral health of PWCF. There is significant variation in the provision of dental treatment and the conditions under which such treatment is provided. Conclusion There is a paucity of information and guidelines specific to this vulnerable population which can make the provision of dental treatment challenging for practitioners. Dental professionals may benefit from continuing professional development and further education targeting patient‐specific populations.

HIV is associated with increased risk of cardiovascular disease, but there has been less study of cardiovascular comorbidities among people with HIV in sub-Saharan Africa. In a cross-sectional observational study, Tanzanian adults presenting for outpatient HIV care completed a questionnaire and underwent weight, height, blood pressure, and blood glucose measurement. Hypertension was defined by blood pressure ≥140/90 mmHg or self-reported hypertension. Uncontrolled hypertension was defined as measured blood pressure ≥140/90 mmHg. Diabetes was defined by fasting glucose ≥126 mg/dl, random glucose ≥200 mg/dl, or self-reported diabetes. Obesity was defined by body mass index ≥30 kg/m2. Multivariate logistic regression was performed to identify predictors of uncontrolled hypertension. Among 500 participants, 173 (34.6%) had hypertension, 21 (4.2%) had diabetes, and 99 (19.8%) were obese. Of those with hypertension, 116 (67.1%) were unaware of their hypertension, and 155 (89.6%) had uncontrolled hypertension. In multivariate analysis, uncontrolled hypertension was associated with older age (OR 1.07, 95% CI: 1.05–1.10, p

This research paper proposes a simple hexa-spiral hexagonal photonic crystal fiber (HSH-PCF) design containing circular timbered air holes, exhibiting a highly nonlinear coefficient and negative dispersion with lower confinement loss. The Finite Element Method (FEM) has been applied here to inspect guiding properties. This propounded HSH-PCF covers all optical bands and windows in the design. The simulation analysis proves that the proposed HSH-PCF achieves a high nonlinear coefficient of 113.8(W.km)-1 at the wavelength of 1.55μm, negligible confinement loss, and the maximum negative dispersion amplitude of -864.5ps.(nm.km)-1. At a 40kW peak power with 20fs input pulse through the 1.5cm of fiber length, the PCF represents a wide-band supercontinuum generation band spectrum. So, the HSH-PCF officiates in several applications like high bit-rate data transfer, amplification of optical parameters, sensor designing of optical fibers, maintaining polarization, dispersion-compensating fibers, spectroscopy system, and other various purposes.

This study introduces a distinctive LiNbO3-based five-ring heptagonal-shaped photonic crystal fiber (PCF) geometry that demonstrates exceptionally high birefringence (Br) and nonlinear coefficient (NLC). Fundamental simulation studies are conducted through the COMSOL software utilizing the finite element method (FEM) with a perfectly matched layer (PML) boundary condition in order to comprehensively explore a variety of relevant optical characteristics, including birefringence, effective area, nonlinearity, core power fraction, dispersion, numerical aperture, confinement loss and so on. The simulation outcomes reveal an ultra-high Br and NLC of 0.081 and 5843.32 W-1Km-1 sequentially, at the operative wavelength of 1.55μm. The designed PCF with ultra-high Br and NLC features can be fabricated suitably, which is an advantageous aspect for potential prospective utilization in polarization-maintaining, nonlinear optics, supercontinuum generation, etc.

A novel single material-based anti-resonant fiber is designed, investigated, and explored in this article with supports of up to 64 OAM modes over the 0.6 μm to 1.0 μm as operating wavelength. The other properties of the designed fiber including confinement loss (CL), OAM purity, effective refractive index differences (ERIDs), and dispersion variations provide the exceptional output. This fiber, the CL approximately varies between the 5.7031 × 10−5 dB/m to 9.2537 × 10−4 dB/m, the OAM purity is 97% to 99%, the ERIDs are higher than 10-4 for all the modes, and the least dispersion variations is −8.838 ps/km-nm for the EH1,1 mode. All the outstanding properties of the presented fiber are calculated using the FEM and the PML within the COMSOL Multiphysics simulator. Therefore, to the ideal of our knowledge, the proposed anti-resonant fiber is mostly applicable in the high-quality long-distance fiber communications system.

Abstract Introduction Monitoring of adherence to antiretroviral treatment (ART) is of utmost importance to prevent treatment failure. Several measures to monitor adherence have been applied in low-resource settings and they all have pros and cons. Our objective was to examine whether any of the following adherence measures is a better predictor of participants’ viral load suppression: (1) self-report, (2) pharmacy refill count, (3) Real Time Medication Monitoring (RTMM), (4) a combination of self-report and pharmacy refill count or (5) all three adherence assessment methods combined. Methodology This was a post-hoc analysis of data from our 48-week REMIND-HIV randomized controlled trial in which adherence to ART was measured using self-report, pharmacy refill counts and RTMM among ART-experienced adults living with HIV subjectively judged to be nonadherent to ART. For each adherence measure, we calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for predicting virological failure defined as a viral load (VL) of > 20 copies/mL. To determine at which percentage of adherence the prediction was strongest, we evaluated adherence cut-offs of 80%, 85%, 90%, 95% and 100% using receiver operating characteristic (ROC) curves. VL data were obtained after 48 weeks of follow-up in the trial. Results A total of 233 people living with HIV (PLHIV) were included in this analysis. When comparing the ability of self-reported adherence with pharmacy refill count and RTMM adherence to predict viral load > 20 copies/ml, self-reported adherence had the lowest sensitivity, ranging from 6 to 17%, but the highest specificity, ranging from 100 to 86%, depending on cut-off values from 80 to 100%. Area under the ROC curves (AUC) were 0.54 for RTMM, 0.56 for pharmacy refill count and 0.52 for self-report, indicating low discriminatory capacity for each of the adherence measures. When we combined the self-report and pharmacy refill count measures, sensitivity increased, ranging from 28 to 57% but specificity decreased, ranging from 83 to 53%. When all three measures were combined, we observed the highest value of sensitivity, ranging from 46 to 92%, and PPV, ranging from 32 to 36%, at high cut-offs ranging from 80 to 100%. Upon combination of three adherence measures, the AUC increased to 0.59. Conclusion Our results show that adherence assessed exclusively by self-report, pharmacy refill count or RTMM were insufficiently sensitive to predict virologic failure. Sensitivity markedly improved by combining all three measures, but the practical feasibility of such an approach would need to be studied.

Abstract The microbiological safety of medical devices is of paramount importance for patients and manufacturers alike. However, during usage medical devices will inevitably become contaminated with microorganisms, including opportunistic pathogens. This is a particular problem if these devices come in contact with body sites that carry high bacterial loads, such as the oral cavity. In the present study, we investigated whether high oxygen concentrations can be applied to disinfect surfaces contaminated with different Gram-positive and Gram-negative bacteria. We show that some opportunistic pathogens, exemplified by Pseudomonas aeruginosa, are particularly sensitive to oxygen concentrations above the atmospheric oxygen concentration of 21%. Our observations also show that high oxygen concentrations can be applied to reduce the load of P. aeruginosa on nebulizers that are used by cystic fibrosis patients, who are particularly susceptible to colonization and infection by this bacterium. We conclude that the efficacy of oxygen-mediated disinfection depends on the bacterial species, duration of oxygen exposure and the oxygen concentration. We consider these observations relevant, because gas mixtures with high oxygen content can be readily applied for microbial decontamination. However, the main challenge for oxygen-based disinfection approaches resides in a potentially incomplete elimination of microbial contaminants, which makes combined usage with other disinfectants like ethanol or hydrogen peroxide recommendable.

Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. From a clinical perspective, the most significant approach to mitigating the effects of diabetes is early-stage control and management, with the aim of a potential cure. However, lack of awareness and expensive clinical tests are the primary reasons why clinical diagnosis and preventive measures are neglected in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, this study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes, due to the fact that clinical features are not always accessible to all people from lower-income countries. To find the best fit of the supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. This study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for developing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.

Abstract Upland moorlands are important landscapes, but many are considered degraded as a result of human activities. Consequently, their protection and restoration are of substantial concern. In Europe, restoration activities are often aimed at reversing the effects of 19th and 20th century “agricultural improvements,” which often involved major drainage schemes. However, the ecological effects and long‐term ecological context of “agricultural improvement” are not yet fully understood. To develop this understanding, we analyze paleoecological data (pollen, coprophilous fungal spores, microcharcoal) from five upland peatland sites using a range of analytical approaches: cluster analysis, principal component analysis, rate‐of‐change analysis, and regression analyses incorporating documentary historical data. The sites are located on Exmoor (South West England, UK), a landscape that typifies historic upland degradation. We demonstrate that in this landscape, 19th century drainage is associated with declines in Sphagnum and non‐arboreal taxon richness; over longer timescales burning is associated with enhanced graminoid monocot abundance and grazing with lower taxon richness. We also show that rate‐of‐change in moorland vegetation communities during the 19th century is not distinctive in a long‐term context: change has been a constant in this landscape, rather than an exception during the 19th century. Our findings indicate that the aims of “restoration” interventions intended to increase Sphagnum abundances, increase taxon richness and reduce graminoid dominance are consistent with the long‐term dynamics of peatland systems, such as those on Exmoor. “Restoration” deemed successful in these terms may or may not resemble pre‐drainage conditions, which were themselves a function of millennia of successive moorland management regimes.