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Abstract Presently, there is considerable emphasis on biological synthesis of nanoparticles containing bioactive reducing compounds with an aim to mitigate the harmful effects of pollutants. The approach under study is simple and ideal for the production of durable antimicrobial nanomaterials by novel single-step green synthesis of TiO2 metal oxide nanostructures using ginger and garlic crude aqueous extracts with bactericidal and catalytic activity. A variety of experimental techniques were used to characterize the synthesized nanomaterials. As demonstrated using x-ray diffraction and ultra-violet visible spectroscopy, the produced nanoparticles exhibited high absorption at 318 nm with size varying between 23.38 nm for ginger and 58.64 nm for garlic in biologically-reduced TiO2. At increasing concentrations (500, 1000 µg/50 µl), nanoparticles reduced with garlic exhibited enhanced bactericidal efficacy against multiple drug-resistant S. aureus and effectively decomposed toxic methylene blue (MB) dye. In conclusion, biologically-reduced TiO2 nanoparticles may prove an effective tool in the fight against microbial illnesses and drug resistance.

Abstract Biomaterials are increasingly being designed and adapted to a wide range of structural applications, owing to their superior mechanical property-to-weight ratios, low cost, biodegradability, and CO2 capture. Bamboo, specifically, has an interesting anatomy with long tube-like vessels present in its microstructure, which can be exploited to improve its mechanical properties for structural applications. By filling these vessels with a resin, e.g. an applied external loading would be better distributed in the structure. One recent method of impregnating the bamboo is plastination, which was originally developed for preserving human remains. However, the original plastination process was found to be slow for bamboo impregnation application, while being also rather complicated/methodical for industrial adaptation. Accordingly, in this study, an improved plastination method was developed that is 40% faster and simpler than the original method. It also resulted in a 400% increase in open-vessel impregnation, as revealed by Micro-X-ray Computed Tomography imaging. The improved method involves three steps: acetone dehydration at room temperature, forced polymer impregnation with a single pressure drop to − 23 inHg, and polymer curing at 130 °C for 20 min. Bamboo plastinated using the new method was 60% stronger flexurally, while maintaining the same modulus of elasticity, as compared to the virgin bamboo. Most critically, it also maintained its biodegradability from cellulolytic enzymes after plastination, as measured by a respirometric technique. Fourier transform infrared-attenuated total reflection, and thermogravimetric analyses were conducted and showed that the plastinated bamboo’s functional groups were not altered significantly during the process, possibly explaining the biodegradability. Finally, using cone calorimetry, plastinated bamboo showed a faster ignition time, due to the addition of silicone, but a lower carbon monoxide yield. These results are deemed as a promising step forward for further improvement and application of this highly abundant natural fiber in engineering structures.

Presently, there is considerable emphasis on biological synthesis of nanoparticles containing bioactive reducing compounds with an aim to mitigate the harmful effects of pollutants. The approach under study is simple and ideal for the production of durable antimicrobial nanomaterials by novel single-step green synthesis of TiO2 metal oxide nanostructures using ginger and garlic crude aqueous extracts with bactericidal and catalytic activity. A variety of experimental techniques were used to characterize the synthesized nanomaterials. As demonstrated using x-ray diffraction and ultra-violet visible spectroscopy, the produced nanoparticles exhibited high absorption at 318 nm with size varying between 23.38 nm for ginger and 58.64 nm for garlic in biologically-reduced TiO2. At increasing concentrations (500, 1000 µg/50 µl), nanoparticles reduced with garlic exhibited enhanced bactericidal efficacy against multiple drug-resistant S. aureus and effectively decomposed toxic methylene blue (MB) dye. In conclusion, biologically-reduced TiO2 nanoparticles may prove an effective tool in the fight against microbial illnesses and drug resistance. [ABSTRACT FROM AUTHOR]

Quantifying the COVID-19 burden is critical to public health response, resource allocation, and policies to mitigate the pandemic, reduce its impact on health and well-being, and ensure recovery and resilience of affected communities. Though a lot of research has been done worldwide on the issues of caregivers during the pandemic, the information about the Pakistani context is not sufficient. The present study aims to explore and quantify the social burden of family caregivers of COVID-19 patients in Pakistan on scientific grounds, which will be a foundation for the policy, support services, and eventually the improvements in the caregivers well-being who are facing the unrepresentative challenges during the pandemic. The present research is a cross-sectional study of family caregivers in Punjab-Pakistan during the pandemic and quantifies the social burden of COVID-19 on caregivers through a 22-item scale developed on a binary response. The study's findings proposed a model of the social burden of caregivers based on five domains . In addition, the study observed significant associations between social burden and some profile variables, such as gender, age, marital status, occupation, income, living arrangements, and caregiving types. The research quantifies the social burden of COVID-19 on family caregivers in Pakistan and will be helpful for researchers, practitioners, and policymakers to understand the social challenges caregivers face during the COVID-19 pandemic in Pakistan., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

A vertical plate experiences a dynamic flow of fractionalized Brinkman fluid governed by fluctuating magnetic forces. This study considers heat absorption and diffusion-thermo effects. The novelty of model is the fractionalized Fourier's and Fick's laws. The problem is solved using the constant proportional Caputo derivative and Laplace transform method. The resulting non-dimensional equations for temperature, mass, and velocity fields are solved and compared visually. We explore the influence of various parameters like the fractional order, heat absorption/generation (Q), chemical reaction rate (R), and magnetic field strength (M) through informative graphs. Additionally, we contrast the velocity fields of fractionalized and regular fluids. The visualizations reveal that diffusion-thermo and mass Grashof number enhance fluid velocity, while chemical reaction and magnetic field tend to suppress it. For the interest of engineering, physical quantities such as Sherwood number, skin friction, and Nusselt number are computed. The present study satisfying all initial and boundary condition can be reduced to to previous published work which shows the validity of present work., (© 2024. The Author(s).)

A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies., (© 2024. The Author(s).)

High-altitude environments present formidable challenges for survival and reproduction, with organisms facing limited oxygen availability and scarce nutrient resources. The yak (Bos grunniens), indigenous to the Tibetan Plateau, has notably adapted to these extreme conditions. This study delves into the genomic basis of the yak's adaptation, focusing on the positive selection acting on genes involved in nutrient assimilation pathways. Employing techniques in comparative genomics and molecular evolutionary analyses, we selected genes in the yak that show signs of positive selection associated with nutrient metabolism, absorption, and transport. Our findings reveal specific genetic adaptations related to nutrient metabolism in harsh climatic conditions. Notably, genes involved in energy metabolism, oxygen transport, and thermoregulation exhibited signs of positive selection, suggesting their crucial role in the yak's successful colonization of high-altitude regions. The study also sheds light on the yak's immune system adaptations, emphasizing genes involved in response to various stresses prevalent at elevated altitudes. Insights into the yak's genomic makeup provide valuable information for understanding the broader implications of high-altitude adaptations in mammalian evolution. They may contribute to efforts in enhancing livestock resilience to environmental challenges., (© 2024. The Author(s).)

As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardiovascular disorders. Machine learning (ML) and deep learning (DL) techniques are pivotal in classifying and identifying heart disease from audio signals. This study investigates ML and DL techniques to detect heart disease by analyzing noisy sound signals. This study employed two subsets of datasets from the PASCAL CHALLENGE having real heart audios. The research process and visually depict signals using spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs). We employ data augmentation to improve the model's performance by introducing synthetic noise to the heart sound signals. In addition, a feature ensembler is developed to integrate various audio feature extraction techniques. Several machine learning and deep learning classifiers are utilized for heart disease detection. Among the numerous models studied and previous study findings, the multilayer perceptron model performed best, with an accuracy rate of 95.65%. This study demonstrates the potential of this methodology in accurately detecting heart disease from sound signals. These findings present promising opportunities for enhancing medical diagnosis and patient care., (© 2024. The Author(s).)

This paper introduces a truncated Weibull-exponential distribution and provides a thorough insight into its mathematical characteristics. These characteristics include moments, generating functions, inverse distribution function, and entropy. Various measures are also discussed about the distribution's reliability. A simulation study is carried out to assess the stability and consistency of the maximum likelihood estimates of the parameters. Finally, two social sciences data sets are used to assess the distribution's relevance in modeling real-world situations., (© 2023. The Author(s).)

In Statistical Process Control, many techniques exist for monitoring the stability of a process over time. In this work, we study the relationship of the response variable with explanatory variables in the form of linear profiles for detecting changes in slope and intercept of the linear quality profiles. We used the transformation of explanatory variables approach used for make the regression estimates independent of each other to have zero average. A comparative study of three phase-II methods using DEWMA statistics in monitoring and capturing undesirable deviations in the slope, intercept, and variability is also studied by applying different proposed run rules schemes i.e., R 1/1 , R 2/3 , R 3/3 . Monte Carlo simulations were carried out on R-Software for finding the results of proposed schemes by taking various levels of shifts for intercept, slope, and standard deviation in identifying the false alarm rate of a process. The simulation results based on the average run length criterion show that the proposed run rule schemes improve the detection ability of the control structure. Among all the proposed schemes R 2/3 is found to be the best one because of its quick detection ability of false alarm rate. The proposed scheme also shows superiority in comparison to other schemes. The simulation results are further justified with a real data application., (© 2023. The Author(s).)

In August 2022, one of the most severe floods in the history of Pakistan was triggered due to the exceptionally high monsoon rainfall. It has affected ~ 33 million people across the country. The agricultural losses in the most productive Indus plains aggravated the risk of food insecurity in the country. As part of the loss and damage (L&D) assessment methodologies, we developed an approach for evaluating crop-specific post-disaster production losses based on multi-sensor satellite data. An integrated assessment was performed using various indicators derived from pre- and post-flood images of Sentinel-1 (flood extent mapping), Sentinel-2 (crop cover), and GPM (rainfall intensity measurements) to evaluate crop-specific losses. The results showed that 2.5 million ha (18% of Sindh's total area) was inundated out of which 1.1 million ha was cropland. The remainder of crop damage came from the extreme rainfall downpour, flash floods and management deficiencies. Thus approximately 57% (2.8 million ha) of the cropland was affected out of the 4.9 million ha of agricultural area in Sindh. The analysis indicated expected production losses of 88% (3.1 million bales), 80% (1.8 million tons), and 61% (10.5 million tons) for cotton, rice, and sugarcane. This assessment provided useful tools to evaluate the L&D of agricultural production and to develop evidence-based policies enabling post-flood recovery, rehabilitation of people and restoration of livelihood., (© 2023. The Author(s).)

Throughout the world, livestock predation by mammalian carnivores causes significant economic losses to poor farmers, and leads to human-wildlife conflicts. These conflicts result in a negative attitude towards carnivore conservation and often trigger retaliatory killing. In northern Pakistan, we investigated livestock depredation by large carnivores between 2014 and 2019, and subsequent Human-wildlife conflict, through questionnaire-based surveys (n = 100 households). We used a semi-structured questionnaire to collect data on livestock population, depredation patterns, predation count, and conservation approaches. We found a statistically significant increasing pattern of predation with influential factors such as age, gender, occupation, education of respondents, population of predators, threats index for predators and conservation efforts. Some 310 livestock heads with an average of 51 animals per year out of the total 9273 heads were killed by predators, and among them 168 (54%) were attributed to the wolf and 142 (45.8%) to snow leopard. Major threats to carnivores in the area included retaliatory killing, habitat destruction and climate change. Incentivization against depredation losses, guarded grazing and construction of predator-proof corral may reduce Human-wildlife conflict and both livelihood and predator can be safeguarded in the study area., (© 2021. The Author(s).)

In the current study, APOB (rs1052031) genotype-guided proteomic analysis was performed in a cohort of Pakistani population. A total of 700 study subjects, including Coronary Artery Disease (CAD) patients (n = 480) and healthy individuals (n = 220) as a control group were included in the study. Genotyping was carried out by using tetra primer-amplification refractory mutation system-based polymerase chain reaction (T-ARMS-PCR) whereas mass spectrometry (Orbitrap MS) was used for label free quantification of serum samples. Genotypic frequency of GG genotype was found to be 90.1%, while 6.4% was for GA genotype and 3.5% was for AA genotypes in CAD patients. In the control group, 87.2% healthy subjects were found to have GG genotype, 11.8% had GA genotype, and 0.9% were with AA genotypes. Significant (p = 0.007) difference was observed between genotypic frequencies in the patients and the control group. The rare allele AA was found to be strongly associated with the CAD [OR: 4 (1.9-16.7)], as compared to the control group in recessive genetic model (p = 0.04). Using label free proteomics, altered expression of 60 significant proteins was observed. Enrichment analysis of these protein showed higher number of up-regulated pathways, including phosphatidylcholine-sterol O-acyltransferase activator activity, cholesterol transfer activity, and sterol transfer activity in AA genotype of rs562338 (G>A) as compared to the wild type GG genotype. This study provides a deeper insight into CAD pathobiology with reference to proteogenomics, and proving this approach as a good platform for identifying the novel proteins and signaling pathways in relation to cardiovascular diseases., (© 2021. The Author(s).)

This study investigated the environmental spatial heterogeneity of novel coronavirus (COVID-19) and spatial and temporal changes among the top-20 metropolitan cities of the Asia-Pacific. Remote sensing-based assessment is performed to analyze before and during the lockdown amid COVID-19 lockdown in the cities. Air pollution and mobility data of each city (Bangkok, Beijing, Busan, Dhaka, Delhi, Ho Chi Minh, Hong Kong, Karachi, Mumbai, Seoul, Shanghai, Singapore, Tokyo, Wuhan, and few others) have been collected and analyzed for 2019 and 2020. Results indicated that almost every city was impacted positively regarding environmental emissions and visible reduction were found in Aerosol Optical Depth (AOD), sulfur dioxide (SO 2 ), carbon monoxide (CO), and nitrogen dioxide (NO 2 ) concentrations before and during lockdown periods of 2020 as compared to those of 2019. The highest NO 2 emission reduction (~ 50%) was recorded in Wuhan city during the lockdown of 2020. AOD was highest in Beijing and lowest in Colombo (< 10%). Overall, 90% movement was reduced till mid-April, 2020. A 98% reduction in mobility was recorded in Delhi, Seoul, and Wuhan. This analysis suggests that smart mobility and partial shutdown policies could be developed to reduce environmental pollutions in the region. Wuhan city is one of the benchmarks and can be replicated for the rest of the Asian cities wherever applicable., (© 2021. The Author(s).)

We aimed to isolate Acinetobacter baumannii (A. baumannii) from wound infections, determine their resistance and virulence profile, and assess the impact of Silver nanoparticles (AgNPs) on the bacterial growth, virulence and biofilm-related gene expression. AgNPs were synthesized and characterized using TEM, XRD and FTIR spectroscopy. A. baumannii (n = 200) were isolated and identified. Resistance pattern was determined and virulence genes (afa/draBC, cnf1, cnf2, csgA, cvaC, fimH, fyuA, ibeA, iutA, kpsMT II, PAI, papC, PapG II, III, sfa/focDE and traT) were screened using PCR. Biofilm formation was evaluated using Microtiter plate method. Then, the antimicrobial activity of AgNPs was evaluated by the well-diffusion method, growth kinetics and MIC determination. Inhibition of biofilm formation and the ability to disperse biofilms in exposure to AgNPs were evaluated. The effect of AgNPs on the expression of virulence and biofilm-related genes (bap, OmpA, abaI, csuA/B, A1S&#95;2091, A1S&#95;1510, A1S&#95;0690, A1S&#95;0114) were estimated using QRT-PCR. In vitro infection model for analyzing the antibacterial activity of AgNPs was done using a co-culture infection model of A. baumannii with human fibroblast skin cell line HFF-1 or Vero cell lines. A. baumannii had high level of resistance to antibiotics. Most of the isolates harbored the fimH, afa/draBC, cnf1, csgA and cnf2, and the majority of A. baumannii produced strong biofilms. AgNPs inhibited the growth of A. baumannii efficiently with MIC ranging from 4 to 25 µg/ml. A. baumannii showed a reduced growth rate in the presence of AgNPs. The inhibitory activity and the anti-biofilm activity of AgNPs were more pronounced against the weak biofilm producers. Moreover, AgNPs decreased the expression of kpsMII , afa/draBC,bap, OmpA, and csuA/B genes. The in vitro infection model revealed a significant antibacterial activity of AgNPs against extracellular and intracellular A. baumannii. AgNPs highly interrupted bacterial multiplication and biofilm formation. AgNPs downregulated the transcription level of important virulence and biofilm-related genes. Our findings provide an additional step towards understanding the mechanisms by which sliver nanoparticles interfere with the microbial spread and persistence.

Two-dimensional (2D) heterostructure with atomically sharp interface holds promise for future electronics and optoelectronics because of their multi-functionalities. Here we demonstrate gate-tunable rectifying behavior and self-powered photovoltaic characteristics of novel p-GeSe/n-MoSe 2 van der waal heterojunction (vdW HJ). A substantial increase in rectification behavior was observed when the devices were subjected to gate bias. The highest rectification of ~ 1 × 10 4 was obtained at V g  = - 40 V. Remarkable rectification behavior of the p-n diode is solely attributed to the sharp interface between metal and GeSe/MoSe 2 . The device exhibits a high photoresponse towards NIR (850 nm). A high photoresponsivity of 465 mAW -1 , an excellent EQE of 670%, a fast rise time of 180 ms, and a decay time of 360 ms were obtained. Furthermore, the diode exhibits detectivity (D) of 7.3 × 10 9 Jones, the normalized photocurrent to the dark current ratio (NPDR) of 1.9 × 10 10  W -1 , and the noise equivalent power (NEP) of 1.22 × 10 -13 WHz -1/2 . The strong light-matter interaction stipulates that the GeSe/MoSe 2 diode may open new realms in multi-functional electronics and optoelectronics applications.