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This article discusses the role of university academics as researchers. In present-day society which touts instant gratification, the primary role of a university is being undermined. In Malaysia, academics no longer teach and research as they please but are 'guided' by government agencies and influenced by the priorities of funding agencies. One of the components of academic freedom is the freedom to publish. Academics publish research that pushes the boundaries of knowledge. They choose journals in which their articles will be peer-reviewed, published and read by the communities of interest. However, lately many academics tend to publish in journals based on rankings, in journals reputed to have higher impact than others. Often young academics are puzzled whether they should publish where it matters or where it would quickly boost the key performance indicators (KPIs) set by the university. This article highlights some of these struggles in modern academia and exposes several examples of academic misconduct.

The q-Rung Orthopair Fuzzy set (qROF-set) environment is a contemporary tool for handling uncertainty and vagueness in decision-making scenarios. In this paper, we delve into the algebraic examination of q-rung orthopair Multi-Fuzzy Sets (MFSs) and explore their operational laws. The novel q-Rung Orthopair Multi-Fuzzy subgroup (qROMF-subgroup) is the extension of Intuitionistic Multi-Fuzzy Subgroup (IMF-subgroup) to encompass the domain of groups. The properties of the proposed fuzzy subgroup are examined in detail, and the paper concludes by defining two additional concepts: qROMF-coset and qROMF-normal subgroup. Finally, we present a comparison of the newly introduced model with existing approaches to validate its superior performance.

Background: Veno-arterial extra-corporeal membrane oxygenation (VA-ECMO) may cause adverse effects including increased left ventricular (LV) filling pressure, LV distension, and pulmonary edema. We aimed to quantify the effects of ECMO flow, LV contractility, aortic pressure (AoP), and ECMO configuration on left atrial pressure (LAP) during VA-ECMO for cardiogenic shock in a mock circulatory loop (MCL). Methods: An MCL simulated a normal state, LV failure, right ventricular failure, and biventricular failure. The ECMO return cannula was placed in the femoral artery (retrograde flow) or ascending aorta (antegrade flow). ECMO flow was incrementally increased from 0 to 5 liter/min. LAP, mean AoP, ECMO flow, and total cardiac output were measured at steady state. Results: During VA-ECMO, LAP increased linearly with AoP, with the slope greater in the presence of LV impairment compared to preserved LV function. When AoP was held constant, as is the goal of therapy in clinical management, ECMO flow had no effect on LAP. In multivariable linear regression, AoP and LV contractility (p

Aim: This study assessed the clinical impact and cost–effectiveness of switching from tenofovir disoproxil fumarate (TDF) to either tenofovir alafenamide (TAF) or entecavir (ETV) in a Greek chronic hepatitis B (CHB) population. Patients & methods: A Markov model from the perspective of a third-party payer in Greece quantified the health and economic benefits of switching from TDF to either TAF or ETV over a lifetime horizon. Results: Over a lifetime, patients who switch from TDF to TAF versus patients who switch from TDF to ETV had an overall lower incidence of compensated cirrhosis (0.4% lower), decompensated cirrhosis (0.04% lower) and hepatocellular carcinoma (0.25% lower). Chronic kidney disease and endstage renal disease were also lower in patients who switch to TAF; major osteoporotic fractures were similar for both groups. While total costs were higher for switching from TDF to TAF versus TDF to ETV due to the higher cost of TAF, switching from TDF to TAF versus ETV was cost effective with an incremental cost–effectiveness ratio of €17,113 per quality-adjusted life year. Conclusion: Switching from TDF to TAF in patients living with CHB is a cost effective strategy to reduce adverse liver disease outcomes, while improving bone- and renal-related safety outcomes.

The developed novel Nanoclay (NC) filled bamboo fibre (BF) /glass fibre (GF) reinforced unsaturated polyester (USP) hybrid composites were subjected to static and dynamic mechanical studies. Effect of NC on mechanical properties of the neat and NC filled BF/GF hybrid composites were tested in accordance with the ASTM standards. Dynamic mechanical analysis was performed on the composites to evaluate their viscoelastic properties like, storage modulus (E’), loss modulus (E”) and damping factor (Tanδ). Outcomes of the mechanical results revealed that, NC filled BF/GF-USP hybrid composites showed superior mechanical performance over the neat hybrid composites particularly 5 wt.% NC filled BF/GF hybrid composite (HC4) outperformed other composites. From the dynamic mechanical it was found that the addition of NC filler in the hybrid composites enhanced the E’, E” and glass transition temperature (Tg). Fractured specimens of the hybrid composites were examined using Field emission electron microscope (FESEM). Matrix fracture, micro-cracks, fibre pullouts, stretching of fibres and delamination of the fibre layers were significantly noted in the fractography analysis. Therefore, from the obtained results, NC filled BF/GF USP hybrid nanocomposites can be employed for automotive applications.

India is one of the leading economies that have been stuck hard by the COVID-19 pandemic and the stringent measures were put in place to combat it. Among several sectors, dairy sector is the most affected as dairy products are highly perishable and rely on time-sensitive supply chains. Though studies are available on the impact of COVID-19 pandemic on dairy sector, there are no studies on COVID-infected dairy farm households. The present study was an attempt to assess the socio-economic impact of COVID-19 pandemic on infected and uninfected dairy farm households in West Bengal. The study covered pre-lockdown, lockdown (both 1st and 2nd wave) and post-lockdown phases of COVID-19 pandemic. The primary data was collected from 150 dairy farm households (COVID-19 infected-75 and uninfected-75) in Murshidabad and Nadia districts of West Bengal. Dairy Economic Performance Index consisting of number of milch animals, milk yield, marketed milk, milk procurement price, concentrate price and veterinary cost was developed using principal component analysis. In order to make infected and uninfected groups statistically comparable, propensity score matching technique was employed. The index values were compared between matched infected and uninfected groups over different phases of COVID-19 pandemic. Dairy households incurred significant economic losses during the lockdown and post-lockdown periods due to increase in cost of concentrates, decline in the number of milch animals and drop in milk procurement prices. Dairy households faced constraints in procuring dry fodder, concentrate feed and in accessing veterinary care. COVID-19 infected dairy farm households had a greater socio-economic hurdle than that of uninfected households.

Summary: Background: Acute kidney injury (AKI), particularly community-acquired AKI (CA-AKI), is a major health concern globally. The International Society of Nephrology's “0 by 25” initiative to reduce preventable deaths from AKI to zero by 2025 is not achievable in low and middle income countries, such as India, possibly due to a lack of data and measures to tackle this urgent public health issue. In India, CA-AKI predisposes younger patients to hospitalization, morbidity, and mortality. This is the first multicenter, prospective, cohort study investigating CA-AKI and its consequences in India. Methods: This study included data from patients with CA-AKI (>12 years of age) housed in the Indian Society of Nephrology-AKI registry, involving 9 participating tertiary care centers in India, for the period between November 2016 and October 2019. The etiological spectrum and renal and patient outcomes of CA-AKI at the index visit and at 1-month and 3-month follow-ups were analyzed. The impact of socioeconomic status (SES) on outcomes was also analyzed. Findings: Data from 3711 patients (mean [±SD] age 44.7 ± 16.5 years; 66.6% male) were analyzed. The most common comorbidities included hypertension (21.1%) and diabetes (19.1%). AKI occurred in medical, surgical, and obstetrical settings in 86.7%, 7.3%, and 6%, respectively. The most common causes of AKI were associated with sepsis (34.7%) and tropical fever (9.8%). Mortality at the index admission was 10.8%. Complete recovery (CR), partial recovery (PR), and dialysis dependency among survivors at the time of discharge were 22.1%, 57.7%, and 9.4%, respectively. Overall, at 3 months of follow-up, mortality rate, CR, PR, and dialysis dependency rates were 11.4%, 72.2%, 7.2%, and 1%, respectively. Multivariate analysis revealed that age >65 years, alcoholism, anuria, hypotension at presentation, thrombocytopenia, vasopressor use, transaminitis, and low SES were associated with mortality at the index admission. Interpretation: Sepsis and tropical fever were the most common causes of CA-AKI. Presentation of CA-AKI to tertiary care units was associated with high mortality, and a significant number of patients progressed to CKD. Individuals with a low SES had increased risk of mortality and require immediate attention and intervention. Funding: This study was funded by the Indian Society of Nephrology.

This paper presents exact Semi-Definite Program (SDP) reformulations for infinite-dimensional moment optimization problems involving a new class of piecewise Sum-of-Squares (SOS)-convex functions and projected spectrahedral support sets. These reformulations show that solving a single SDP finds the optimal value and an optimal probability measure of the original moment problem. This is done by establishing an SOS representation for the non-negativity of a piecewise SOS-convex function over a projected spectrahedron. Finally, as an application and a proof-of-concept illustration, the paper presents numerical results for the Newsvendor and revenue maximization problems with higher-order moments by solving their equivalent SDP reformulations. These reformulations promise a flexible and efficient approach to solving these models. The main novelty of the present work in relation to the recent research lies in finding the solution to moment problems, for the first time, with piecewise SOS-convex functions from their numerically tractable exact SDP reformulations.

We aimed to analyse the association between processed food consumption and the risk of non-communicable diseases (NCDs) in South Africa. In this empirical study, we analysed nationally representative secondary data obtained from the South African Demographic and Health Survey (SADHS) VII. The survey included 13,288 occupied households, of which 11,083 were interviewed. In the interviewed households, 12,717 eligible adults aged 15 and older were identified and 10,336 were successfully interviewed. The study included four processed food groups (i.e. fried foods, takeaway foods/fast foods, salty snacks/packed chips, and processed meats) and eight NCDs (i.e. hypertension, cardiac arrest, cancer, stroke, hypercholesterolaemia, diabetes, chronic bronchitis, and asthma). As per the logistic regression results following adjustment, none of the disease states showed association with all four processed food groups. However, at least three processed food groups showed a significant positive association with hypertension, cardiac arrest, and diabetes. Two processed food groups showed significant positive association with stroke, and chronic bronchitis; one with hypercholesterolaemia and asthma; and cancer was not associated with any food groups. Processed meat and salted snacks/packed chips were each associated with five chronic conditions. In summary, we found that the consumption of any of the processed food groups increased the risk of NCDs in the South African population. Enabling policy and regulatory efforts in the production and distribution of processed foods, combined with improved awareness among the population need to be prioritised for immediate action. Facilitating the populations to choose traditional healthy diets would be a sustainable strategy for the prevention of NCDs.

Recent advances in gene editing are enabling the engineering of cells with an unprecedented level of scale. To capitalize on this opportunity, new methods are needed to accelerate the different steps required to manufacture and handle engineered cells. Here, we describe the development of an integrated software and hardware platform to automate Fluorescence-Activated Cell Sorting (FACS), a central step for the selection of cells displaying desired molecular attributes. Sorting large numbers of samples is laborious, and, to date, no automated system exists to sequentially manage FACS samples, likely owing to the need to tailor sorting conditions ("gating") to each individual sample. Our platform is built around a commercial instrument and integrates the handling and transfer of samples to and from the instrument, autonomous control of the instrument's software, and the algorithmic generation of sorting gates, resulting in walkaway functionality. Automation eliminates operator errors, standardizes gating conditions by eliminating operator-to-operator variations, and reduces hands-on labor by 93%. Moreover, our strategy for automating the operation of a commercial instrument control software in the absence of an Application Program Interface (API) exemplifies a universal solution for other instruments that lack an API. Our software and hardware designs are fully open-source and include step-by-step build documentation to contribute to a growing open ecosystem of tools for high-throughput cell biology.

Information on genetic diversity and population structure is helpful to strategize enhancing the genetic base of hybrid parental lines in breeding programs. The present study determined the population structure and genetic diversity of 109 pearl millet hybrid parental lines, known for their better adaptation and performance in drought-prone environments, using 16,472 single nucleotide polymorphic (SNP) markers generated from GBS (genotyping-by-sequencing) platforms. The SNPs were distributed uniformly across the pearl millet genome and showed considerable genetic diversity (0.337), expected heterozygosity (0.334), and observed heterozygosity (0.031). Most of the pairs of lines (78.36%) had Identity-by-State (IBS) based genetic distances of more than 0.3, indicating a significant amount of genetic diversity among the parental lines. Bayesian model-based population stratification, neighbor-joining phylogenetic analysis, and principal coordinate analysis (PCoA) differentiated all hybrid parental lines into two clear-cut major groups, one each for seed parents (B-lines) and pollinators (R-lines). Majority of parental lines sharing common parentages were found grouped in the same cluster. Analysis of molecular variance (AMOVA) revealed 7% of the variation among subpopulations, and 93% of the variation was attributable to within sub-populations. Chromosome 3 had the highest number of LD regions. Genomic LD decay distance was 0.69 Mb and varied across the different chromosomes. Genetic diversity based on 11 agro-morphological and grain quality traits also suggested that the majority of the B- and R-lines were grouped into two major clusters with few overlaps. In addition, the combined analysis of phenotypic and genotypic data showed similarities in the population grouping patterns. The present study revealed the uniqueness of most of the inbred lines, which can be a valuable source of new alleles and help breeders to utilize these inbred lines for the development of hybrids in drought-prone environments.

Luminescence is ubiquitous in biology research and medicine. Conceptually simple, the detection of luminescence nonetheless faces technical challenges because relevant signals can exhibit exceptionally low radiant power densities. Although low light detection is well-established in centralized laboratory settings, the cost, size, and environmental requirements of high-performance benchtop luminometers are not compatible with geographically-distributed global health studies or resource-constrained settings. Here we present the design and application of a ~$700 US handheld, battery-powered luminometer with performance on par with high-end benchtop instruments. By pairing robust and inexpensive Silicon Photomultiplier (SiPM) sensors with a low-profile shutter system, our design compensates for sensor non-idealities and thermal drift, achieving a limit of detection of 1.6E-19 moles of firefly luciferase. Using these devices, we performed two pilot cross-sectional serology studies to assess sars-cov-2 antibody levels: a cohort in the United States, as well as a field study in Bangladesh. Results from both studies were consistent with previous work and demonstrate the device's suitability for distributed applications in global health.

IntroductionFungal keratitis (FK) poses a severe threat to vision, potentially leading to blindness if not promptly addressed. Clitoria ternatea flower extracts have a history of use in Ayurvedic and Indian traditional medicines, particularly for treating eye ailments. This study investigates the antifungal and antibiofilm effects of Clitoria ternatea flower extracts on the FK clinical isolate Coniochaeta hoffmannii. Structural details and key compound identification were analysed through FTIR and GC-MS.MethodsThe minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC) of Clitoria ternatea flower extracts were determined using broth dilution and well plate techniques. Biofilm inhibitory activity was assessed through microscopic evaluation, while anti-irritant and cytotoxic properties were evaluated using CAE-EI and MTT assays. Through GC-MS and FT-IR analysis the compounds dissolved in the extract and their functional group were studied, and their toxicity screening and pharmacokinetic prediction were conducted in silico. Subsequently, compounds with high corneal permeability were further identified, and molecular docking and simulation studies at 150 ns were used to investigate their interactions with fungal virulence factors and human inflammatory proteins.Results and DiscussionAt a concentration of 250 µg/mL, the Clitoria ternatea flower extract displayed effective biofilm inhibition. MIC and MFC values were determined as 500 and 1000 µg/mL, respectively. CAE-EI and MTT assays indicated no significant irritant and cytotoxic effects up to a concentration of 3 mg/mL. Compounds like 9,9-dimethoxybicyclo[3.3.1]nonane-2,4-dione showed high corneal permeability with strong and stable interactions with fungal virulence cellobiose dehydrogenase, endo β 1,4 xylanase, and glucanase, as well as corneal inflammation-associated human TNF-α and Interleukin IL-1b protein targets. The findings indicate that extracts from C. ternatea flowers could be formulated for an effective and safe alternative for developing new topical FK therapeutics.

Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition that manifests in early childhood, in which the maternal metabolic syndrome may be a risk factor. The kidney is a barometer of maternal metabolic syndrome duration and severity. Objective: The main objective of this study is to determine whether periconceptional kidney function is associated with ASD in early childhood. Design, Setting, and Participants: This retrospective population-based cohort study was completed in Ontario, Canada. Included were singleton children born in an Ontario hospital between April 2007 and March 2021, who were alive at age 48 months and whose mother had a recorded prepregnancy body mass index (BMI) and a measured serum creatinine (SCr) between 120 days preconception and 28 days postconception. Measurement: The main study outcome was a diagnosis of ASD between ages 24 and 48 months. Methods: Relative risks (RRs) of ASD in association with periconceptional SCr were generated using modified Poisson regression and adjusted for several confounders. Results: The cohort comprised 86 054 women, who had 89 677 liveborn children surviving to at least 48 months of age. There was no significant association between periconceptional SCr and ASD (RR: 0.86; 95 % confidence interval: [0.67, 1.10]). Limitations: Selection bias may have arisen had SCr been ordered on clinical grounds. Conclusions: Further study is warranted to determine whether prepregnancy glomerular hyperfiltration is a marker of ASD and other behavioral conditions in childhood. To do so, a more accurate measure of hyperfiltration is needed than SCr.

BackgroundArtificial intelligence (AI) is transforming the mental health care environment. AI tools are increasingly accessed by clients and service users. Mental health professionals must be prepared not only to use AI but also to have conversations about it when delivering care. Despite the potential for AI to enable more efficient and reliable and higher-quality care delivery, there is a persistent gap among mental health professionals in the adoption of AI. ObjectiveA needs assessment was conducted among mental health professionals to (1) understand the learning needs of the workforce and their attitudes toward AI and (2) inform the development of AI education curricula and knowledge translation products. MethodsA qualitative descriptive approach was taken to explore the needs of mental health professionals regarding their adoption of AI through semistructured interviews. To reach maximum variation sampling, mental health professionals (eg, psychiatrists, mental health nurses, educators, scientists, and social workers) in various settings across Ontario (eg, urban and rural, public and private sector, and clinical and research) were recruited. ResultsA total of 20 individuals were recruited. Participants included practitioners (9/20, 45% social workers and 1/20, 5% mental health nurses), educator scientists (5/20, 25% with dual roles as professors/lecturers and researchers), and practitioner scientists (3/20, 15% with dual roles as researchers and psychiatrists and 2/20, 10% with dual roles as researchers and mental health nurses). Four major themes emerged: (1) fostering practice change and building self-efficacy to integrate AI into patient care; (2) promoting system-level change to accelerate the adoption of AI in mental health; (3) addressing the importance of organizational readiness as a catalyst for AI adoption; and (4) ensuring that mental health professionals have the education, knowledge, and skills to harness AI in optimizing patient care. ConclusionsAI technologies are starting to emerge in mental health care. Although many digital tools, web-based services, and mobile apps are designed using AI algorithms, mental health professionals have generally been slower in the adoption of AI. As indicated by this study’s findings, the implications are 3-fold. At the individual level, digital professionals must see the value in digitally compassionate tools that retain a humanistic approach to care. For mental health professionals, resistance toward AI adoption must be acknowledged through educational initiatives to raise awareness about the relevance, practicality, and benefits of AI. At the organizational level, digital professionals and leaders must collaborate on governance and funding structures to promote employee buy-in. At the societal level, digital and mental health professionals should collaborate in the creation of formal AI training programs specific to mental health to address knowledge gaps. This study promotes the design of relevant and sustainable education programs to support the adoption of AI within the mental health care sphere.

BackgroundThere is growing interest in enhancing stroke self-management support using mobile health (mHealth) technology (eg, smartphones and apps). Despite this growing interest, “self-management support” is inconsistently defined and applied in the poststroke mHealth intervention literature, which limits efforts to synthesize and compare evidence. To address this gap in conceptual clarity, a scoping review was conducted. ObjectiveThe objectives were to (1) identify and describe the types of poststroke mHealth interventions evaluated using a randomized controlled trial design, (2) determine whether (and how) such interventions align with well-accepted conceptualizations of self-management support (the theory by Lorig and Holman and the Practical Reviews in Self-Management Support [PRISMS] taxonomy by Pearce and colleagues), and (3) identify the mHealth functions that facilitate self-management. MethodsA scoping review was conducted according to the methodology by Arksey and O’Malley and Levac et al. In total, 7 databases were searched. Article screening and data extraction were performed by 2 reviewers. The data were analyzed using descriptive statistics and content analysis. ResultsA total of 29 studies (26 interventions) were included. The interventions addressed 7 focal areas (physical exercise, risk factor management, linguistic exercise, activities of daily living training, medication adherence, stroke education, and weight management), 5 types of mobile devices (mobile phones or smartphones, tablets, wearable sensors, wireless monitoring devices, and laptops), and 7 mHealth functions (educating, communicating, goal setting, monitoring, providing feedback, reminding, and motivating). Collectively, the interventions aligned well with the concept of self-management support. However, on an individual basis (per intervention), the alignment was less strong. ConclusionsOn the basis of the results, it is recommended that future research on poststroke mHealth interventions be more theoretically driven, more multidisciplinary, and larger in scale.

As emerging evidence indicates that FMD vaccination causes variable degrees of pregnancy loss in the beef cow, the study was conducted at NDRI SRS in sub-tropical conditions in the year 2021 to investigate its effects on the pregnancy rate in the dairy cow when they were bred within 14 days of FMD vaccination. Retrospective data on AI done within 14 days post FMD vaccination (dpv -14 to +14) and the corresponding pregnancy outcome were collected for 6 years (January 2016 to January 2021) and each record was categorized into AI pre-vaccination (dpv -14 to -1) or AI post-vaccination (dpv 0 to 14). After excluding repeat breeding cows and pregnant heifers, the final dataset consisted of 47 records derived from crossbred and Deoni cows. The binary outcome variable pregnancy was regressed on the categorical predictor variables such as day of AI with respect to dpv 0, stage of lactation and parity. The results indicated that the odds of pregnancy in the AI post-vaccinated group was 0.47 (95% confidence interval: 0.10-1.88) value) as compared with AI pre-vaccinated group. It can be concluded that FMD vaccination has no significant effect on pregnancy outcome if the animals are bred 14 days before and 14 days after FMD vaccination (irrespective of breed, age, parity, days in milk).