Search

Abstract Drought stress (DS) adversely affects a plant’s development and growth by negatively altering the plant’s physio-biochemical functions. Previous investigations have illustrated that seed priming with growth regulators is an accessible, affordable, and effective practice to elevate a plant’s tolerance to drought stress. Melatonin (MT) is derived from the precursor tryptophan and can improve germination, biomass, and photosynthesis under stress conditions. The current study examined the effect of melatonin seed priming on two wheat cultivars (Fakhar-e-Bhakkar and Akber-19) cultivated under severe drought conditions (35% FC). There were 6 levels of melatonin (i.e., M0 = control, M1 = 1 mg L− 1, M2 = 2 mg L− 1, M3 = 3 mg L− 1, M4 = 4 mg L− 1 and M5 = mg L− 1) which were used for seed priming. Our results confirmed that seed priming with M2 = 2 mgL− 1 concentration of MT alleviates the negative effects of DS by boosting the germination rate by 54.84% in Akber-19 and 33.33% in Fakhar-e-Bhakkar. Similarly, leaf-relative water contents were enhanced by 22.38% and 13.28% in Akber-19 and Fakhar-e-Bhakkar, respectively. Melatonin pre-treatment with 2 mgL− 1 significantly enhanced fresh and dry biomass of shoot and root, leaf area, photosynthetic pigments, osmoprotectants accumulation [total soluble proteins (TSP), total free amino acids (TFAA), proline, soluble sugars, glycine betaine (GB)] and lowered the amount of malondialdehyde (MDA) and hydrogen peroxide (H2O2) production by elevating antioxidants [Ascorbic acid, catalase (CAT), Phenolics, peroxidase (POD) and superoxide dismutase (SOD)] activity under drought stress (DS). Meanwhile, under control conditions (NoDS), the melatonin treatment M1 = 1 mgL− 1 effectively enhanced all the growth-related physio-biochemical attributes in both wheat cultivars. In the future, more investigations are suggested on different crops under variable agroclimatic conditions to declare 2 mgL− 1 melatonin as an efficacious amendment to alleviate drought stress.

Abstract Global warming has significantly increased the risk of heat waves (HWs) globally, with India being particularly vulnerable during the summer months (March-June; MAMJ). This study investigated the critical relationship between Indian summer monsoon rainfall (ISMR) and the occurrence of premonsoon HWs in subsequent years across the Indian subcontinent. It has been hypothesized that droughts during the ISMR could lead to more frequent HWs in the following MAMJ period. Using the Indian Meteorological Department's (IMD) gridded observed surface air daily maximum temperature (Tmax) dataset for the period 1951–2023, we analyzed the climatic patterns, interannual variability (IAV), and coefficient of variation (CV) of Tmax across India. The analysis compares two distinct periods: 1951–1999 (P1) and 2000–2023 (P2), with focus on Tmax trends and HW duration, distinguishing between short-duration HWs (SHWs, 2 days) and long-duration HWs (LHWs, 5 days or more). A key purpose of this study is to examine the relationship between the preceding all India summer monsoon rainfall (AISMR) and the occurance of various types of HW in the subsequent premonsoon season. In particular extreme AISMR events, such as droughts or excess rainfall, influence HW occurrence. The findings reveal a significant rise in Tmax across many regions of India during the MAMJ period, with the highest temperatures (> 37 °C) observed in northwestern, central, and eastern coastal areas. Northern India, particularly the Himalayan region, exhibits a greater interannual variability in Tmax, with June showing the most pronounced fluctuations. The study also highlights an increase in the frequency and intensity of HWs, especially in central and southern India, with the Chandigarh-Haryana-Delhi region recording the highest occurrences. A critical finding is the strong inverse relationship between the AISMR and conditions in the subsequent premonsoon season. Specifically, drought in the antecedent AISMR results in reduced soil moisture, which is strongly associated with higher premonsoon Tmax and an increased frequency of extreme heat events across India, particularly in regions prone to severe heat during this season. Drought conditions during AISMR are closely linked to higher HW frequencies in the following summer, especially in the central, northeast-central, and east-coastal regions. The frequencies of HW days, SHWs, and LHWs are significantly greater in years following AISMR droughts than in those following excess rainfall, indicating that drought years are more likely to lead to widespread HW activity. Despite the overall warming trends, some regions, such as the Indo-Gangetic Plain and parts of the Himalayan region, show cooling trends, although these trends are less widespread. The onset of the monsoon in June tends to reduce the intensity and spatial extent of warming, particularly in the central and eastern coastal regions, although significant HW trends persist in northwestern India and along the east coast. This study underscores the crucial role of AISMR in influencing HW events across India and highlights the need for adaptive strategies that account for the interactions between monsoon rainfall and HW risk, providing valuable insights for mitigating the impacts of HWs in the context of global warming.

Abstract Vitamin D deficiency is common in many societies and causes rickets and non-skeletal disorders in children. There are no published data on vitamin D deficiency in Afghanistan. We, therefore, investigated the prevalence and associated factors of vitamin D deficiency in Afghan school children in Kandahar City, Afghanistan. This cross-sectional analytical study was conducted from September 2022 to April 2023 in 510 primary school students aged 6–15 years from six randomly selected schools. Data were analyzed by using descriptive statistics, Chi-square test, and multivariate logistic regression. Of the 510 enrolled children, 54.3% were boys and 91.8% were poor. The mean serum 25(OH)D concentration was 9.3 ng/mL. Vitamin D deficiency (

Ganglionic cysts are benign soft-tissue lesions with unclear etiology and pathogenesis commonly occurring on the wrists and hands. Although their diagnosis and management in these characteristic locations are easy, rare occurrences in atypical locations are difficult to diagnose. Ultrasonography and magnetic resonance imaging (MRI) are helpful in diagnosing and differentiating a ganglionic cyst from other soft-tissue lesions. Here, we report a case of 60-year-old male patient with intramuscular ganglionic cyst, measuring 68×41×48 mm, arising in the extensor digitorum muscle of the left leg, which was diagnosed using MRI and treated using excision and biopsy.

Abstract Background Hepatitis C virus (HCV) antibody self-testing (HCVST) may help expand screening access and support HCV elimination efforts. Despite potential benefits, HCVST is not currently implemented in Pakistan. This study aimed to assess the usability and acceptability of HCVST in a high HCV prevalence informal settlement in Karachi, Pakistan. Methods We performed a cross-sectional study in a hepatitis C clinic from April through June 2023. Participants were invited to perform a saliva-based HCVST (OraSure Technologies, USA) while following pictorial instructions. A study member evaluated test performance using a standardized checklist and provided verbal support if a step could not be completed. Perceived usability and acceptability were assessed using a semi-structured questionnaire. The HCVST was considered successful if the participant was able to complete all steps and correctly interpret test results. Overall concordance and positive and negative agreement were estimated in comparison with the HCVST result read by the study member (inter-reader concordance and agreement) and result of a second rapid HCV test (Abbott Diagnostics Korea Inc, South Korea) performed by a trained user (inter-operator concordance and agreement). Results The study included 295 participants of which 97 (32%) were illiterate. In total, 280 (95%, 95% CI 92–97%) HCVSTs were successful. Overall, 38 (13%) people performed the HCVST without verbal assistance, 67 (23%) needed verbal assistance in one step, 190 (64%) in two or more. Assistance was most often needed in managing the test buffer and test reading times. The inter-reader concordance was 96% and inter-operator concordance 93%. Inter-reader and inter-operator positive percent agreement were 84 and 70%, respectively. All participants reported they would use HCVST again and would recommend it to friends and family. Conclusion Saliva-based HCVST was very well accepted in this clinic-based setting. However, many people requested verbal support in several steps, highlighting the need for clear instructions for use and test devices that are simple to use, particularly in low literacy settings. Moderately low positive percent agreement with the results of a rapid test performed by a trained user highlights potential uncertainty in the accuracy of HCVST in the hands of lay users.

Abstract Background Skin cancer continues to be an imperative global health issue, urging continuous exploration of treatment methodologies. Conventional treatments for skin cancer include surgical interventions, immunotherapy, targeted therapy, chemotherapy, and radiation therapy. However, these methods often present obstacles like treatment resistance, systemic toxicity, limited effectiveness in advanced stages, infection risk, pain, long recovery, and impact on healthy tissue. Main body of the abstract Nanomedicine holds promise by facilitating precise drug administration, early detection, and heightened therapeutic efficiency via targeted and localized delivery systems. The integration of nanomedicine into skin cancer alleviation therapies demonstrates optimistic outcomes, including refined drug delivery, augmented bioavailability, minimized adverse effects, and potential theranostic applications. Recent breakthroughs in nanomedicine have propelled advancements in skin cancer treatment, showing significant potential in transforming the treatment paradigm. The presents review provides comprehensive aspects of existing skin cancer treatments and their challenges, spotlighting recent breakthroughs propelled by nanomedicine. Short conclusion This abstract delineates the present landscape of skin cancer treatments, underscores their constraints, and highlights recent strides in nanomedicine that have the potential to transform the paradigm of skin cancer treatment, ultimately elevating patient prognosis. Importantly, the present review emphasizes substantial challenges that hinder the clinical translation of nanomedicines and suggests possible remedies to surpass them. Graphic abstract

Abstract Background Undernutrition, which includes stunting, wasting, and underweight, is a global problem, especially among children of low- and middle-income countries. To our knowledge, this study is first of its type from Afghanistan. Its main objectives were to estimate the prevalence and associated risk factors of stunting, wasting/thinness, and underweight among urban primary school children in Kandahar city of Afghanistan. Methods This school-based cross-sectional study was conducted among 1205 primary school children aged 6–12 years during a period of six months (October 2022–March 2023). Anthropometric measurements and other data were collected from all the participants. Data were analyzed by using descriptive statistics, Chi square test (using crude odds ratio or COR), and multivariate logistic regression (using adjusted odds ratio or AOR). Results Among the 1205 enrolled government school students, 47.4%, 19.5%, and 25.6% had stunting, wasting/thinness, and underweight, respectively. Statistically significant factors associated with stunting were age group 6–9 years (AOR 1.3, 95% CI 1.1–1.7), being girl (AOR 2.3, 95% CI 1.8–3.0), poverty (AOR 2.2, 95% CI 1.5–3.2), large family (AOR 3.0, 95% CI 2.4–3.9), illiterate mother (AOR 1.6, 95% CI 1.0–2.6), jobless head of the family (AOR 3.3, 95% CI 2.3–4.8), and skipping breakfasts (AOR 1.7, 95% CI 1.2–2.3). Main factor associated with wasting/thinness were age group 6–9 years (AOR 30.5, 95% CI 11.8–78.7), skipping breakfasts (AOR 22.9, 95% CI 13.9–37.8), and history of sickness during the past two weeks (AOR 17.0, 95% CI 6.6–43.8). Also, main factors associated with underweight were age group 6–9 years (AOR 2.6, 95% CI 1.6–4.1), skipping breakfasts (AOR 2.6, 95% CI 1.8–3.6), and poor sanitation (AOR 1.9, 95% CI 1.1–3.2). Conclusions Stunting, wasting/thinness, and underweight are highly prevalent among primary school children (both girls and boys) in Kandahar city. It is recommended that local government (Afghanistan Ministry of Education and Ministry of Public Health) with the help of international organizations and donor agencies should implement comprehensive school-based feeding programs especially for girls. Health and nutrition education programs should be conducted with emphasis on nutrition of children aged 6–9 years as well as importance of healthy breakfast and good sanitation.

Humans' indiscriminate usage of heavy metals has a negative impact on the world's natural ecosystem. One of the main causes of contaminating the fresh and healthy atmosphere in rural Pakistan is the use of coal and soil in an improper manner by brick kilns. Different heavy metal concentrations and their impact on the surrounding areas of Pakistan's District Lakki Marwat were investigated in this study. Acid digestion methods were used to prepare samples, which were then examined using an Atomic Absorption Spectrophotometer. The concentrations of cadmium, iron, and lead in certain samples exceeded the WHO's permitted level, whereas nickel and zinc concentrations were within the limit. It was also discovered that the supply of raw materials is the key determinant in the buildup of heavy metals in the areas near brick not the age of the kiln.

Empirical studies based on detailed, theory-based analyses are essential for a deep understanding of technology adoption. This study provides an overview of blockchain applications in logistics management, employing a comprehensive theoretical framework. Blockchain is considered a critical digital infrastructure for logistics operations due to its distinctive characteristics, including decentralization, transparency, immutability, real-time information sharing, reliability, and end-to-end visibility. These characteristics address many contemporary logistics challenges. The study introduces a research model that integrates the fit-viability model (FVM) and task technology fit theory (TTF), demonstrating blockchain's suitability for enhancing logistics operational functions and sustainability performance. To validate the model, data were collected from logistics managers of 576 companies and analyzed using partial least squares (PLS) regression. This research offers valuable insights for managers, policymakers, and decision-makers on practical challenges and potential solutions in logistics through the application of blockchain. Furthermore, the study demonstrates that the implementation of blockchain can improve the alignment, resilience, transparency, integration, and sustainability of logistics tasks.

This work investigated the photochemical degradation of malachite green (MG), a cationic triphenylmethane dye used as a coloring agent, fungicide, and antiseptic. UV photolysis was ineffective in the removal of MG as only 12.35% degradation of MG (10 mg/L) was achieved after 60 min of irradiation. In contrast, 100.00% degradation of MG (10 mg/L) was observed after 60 min of irradiation in the presence of 10 mM H2O2 by UV/H2O2 at pH 6.0. Similarly, complete removal (100.00%) of MG was observed at 30 min of the reaction time by UV/H2O2/Fe2+ employing [MG]0 = 10 mg/L, [H2O2]0 = 10 mM, [Fe2+]0 = 2.5 mg/L, and [pH]0 = 3.0. For the UV/H2O2 process, the degradation efficiency was higher at pH 6.0 than at pH 3.0 as the kobs values were 0.0873 and 0.0690 min−1, respectively. However, UV/H2O2/Fe2+ showed higher reactivity at pH 3.0 than at pH 6.0. Chloride and nitrate ions slightly inhibited the removal efficiency of MG by both UV/H2O2 and UV/H2O2/Fe2+ processes. Moreover, three degradation products (DPs) of MG, (i) 4-dimethylamino-benzophenone (DABP), (ii) 4-amino-benzophenone (ABP), and (iii) 4-dimethylamino-phenol (DAP), were identified by GC-MS during the UV/H2O2 treatment. These DPs were found to demonstrate higher aquatic toxicity than the parent MG, suggesting that researchers should focus on the removal of target pollutants as well as their DPs. Nevertheless, the results of this study indicate that both UV/H2O2 and UV/H2O2/Fe2+ processes could be implemented to alleviate the harmful environmental impacts of dye and textile industries.

Abstract A natural product is a compound or substance originating from a living organism and found in nature. Daidzein belongs to the class of bioflavonoids, which are plant‐derived compounds with various biological activities. Predominantly exists in soybeans and several legumes in either glycoside or aglycone forms. Its primary chemical constituents include metabolites like equol and trihydroxy isoflavone, generated through the influence of intestinal bacteria. Daidzein has exhibited pharmacological impacts on different ailments, including cardiovascular disease, cancer, diabetes, skin disorders, osteoporosis, and neurodegenerative disorders. Daidzein's mechanisms of action involve interactions with estrogen receptors, as well as its antioxidant and anti‐inflammatory properties, along with its ability to regulate apoptosis and the cell cycle. In efforts to enhance its solubility, stability, bioavailability, and targeting, daidzein has been innovatively formulated into novel dosage forms, including nanoparticles, liposomes, microemulsions, and nanosuspensions. As a promising nutraceutical, daidzein presents multiple health benefits and holds potential for various clinical applications. Additional investigation is required to comprehend the molecular mechanisms of this phenomenon and assess its safety. The purpose of this review is to provide a short description of the therapeutic properties, chemical composition, traditional use, toxicology profile, new insights on the dosage form, and future prospects of daidzein.

Background: Hepatocellular carcinoma is the most prevalent type of primary liver cancer and remains the foremost cause of cancer-related deaths globally. Dill (Anethum graveolens) seeds, rich in phytoconstituents, is renowned for their pharmacological properties. Objectives: This study performed an in vitro evaluation to assess the cytotoxic effects of dill seed extract (DS-EE) on the Huh-7 hepatocellular carcinoma cell line. Moreover, the study investigated its effects on cell viability, cellular morphology, oxidative damage, levels of intracellular reactive oxygen species (ROS), mitochondrial membrane potential (MMP), and the expression of apoptosis-related genes in Huh-7 cells. Methods: Huh-7 cells were treated with DS-EE at concentrations ranging from 5 to 100 μg/mL for a duration of 24 h. Results: The cytotoxicity findings showed that DS-EE decreased cell viability and suppressed the growth of Huh-7 cells in a dose-dependent way, with an IC50 value of 60 μg/mL. Exposure to DS-EE extract for 24 h resulted in a significant elevation in lipid peroxidation (LPO) and a notable decrease in glutathione (GSH) content compared to the control. Furthermore, DS-EE significantly increased ROS production while notably decreasing the MMP level in Huh-7 cells. Moreover, DS-EE induces cell apoptosis by upregulating the expression of proapoptotic genes (p53, caspase-3, caspase-9, and Bax) and downregulating the expression of the antiapoptotic gene, Bcl-2. Conclusion: DS-EE exhibited a notable cytotoxic effect on Huh-7 cells by increasing oxidative damage and subsequently modulating the expression of apoptosis-related genes. The results of this study highlight the anticancer effectiveness of DS-EE, indicating its potential as a promising agent for hepatocellular carcinoma management.

Background: Mycotoxins found in cereals have adverse effects on animals, humans, and agricultural products, posing significant health risks and affecting the marketability of wheat products. Aim: This study aimed to identify mycotoxin contamination in durum and bread wheat and to evaluate the effects of heating and Saccharomyces cerevisiae co-culture on controlling toxigenic fungal growth and decontaminating mycotoxins in stored wheat products. Methods: Aflatoxins and ochratoxins were extracted from various samples of durum and bread wheat available in the Pakistani market using methanol, water, and n-hexane. Mycotoxins were identified using thin-layer chromatography (TLC). Fungal species were identified using Sabouraud Dextrose Agar (SDA) and Potato Dextrose Agar (PDA), with samples incubated at 28°C for 6 to 7 days. The stability of mycotoxins during thermal processing was assessed using traditional cooking methods. Results: Eight samples with aflatoxins and six samples with ochratoxins had levels above the European Union Commission maximum limits. The sample from Faisalabad containing aflatoxin B2 was notably above the maximum threshold. Thermal treatment completely eliminated mycotoxins, enhancing food safety. Additionally, Saccharomyces cerevisiae co-culture significantly inhibited mycotoxin growth, making the grains safer and increasing their marketable value. Conclusion: Thermal processing effectively eliminates mycotoxins from wheat samples, ensuring food safety. Saccharomyces cerevisiae co-culture is a promising method to inhibit mycotoxin growth, thereby improving the safety and marketability of wheat products.

Ambiguity is a common challenge in specifying natural language (NL) requirements. One of the reasons for the occurrence of ambiguity in software requirements is the lack of user involvement in requirements elicitation and inspection phases. Even if they get involved, it is hard for them to understand the context of the system, and ultimately unable to provide requirements correctly due to a lack of interest. Previously, the researchers have worked on ambiguity avoidance, detection, and removal techniques in requirements. Still, less work is reported in the literature to actively engage users in the system to reduce ambiguity at the early stages of requirements engineering. Traditionally, ambiguity is addressed during inspection when requirements are initially specified in the SRS document. Resolving or removing ambiguity during the inspection is time-consuming, costly, and laborious. Also, traditional elicitation techniques have limitations like lack of user involvement, inactive user participation, biases, incomplete requirements, etc. Therefore, in this study, we have designed a framework, Gamification for Lexical Ambiguity (Gamify4LexAmb), for detecting and reducing ambiguity using gamification. Gamify4LexAmb engages users and identifies lexical ambiguity in requirements, which occurs in polysemy words where a single word can have several different meanings. We have also validated Gamify4LexAmb by developing an initial prototype. The results show that Gamify4LexAmb successfully identifies lexical ambiguities in given requirements by engaging users in requirements elicitation. In the next part of our research, an industrial case study will be performed to understand the effects of gamification on real-time data for detecting and reducing NL ambiguity.

Abstract The installation of sand screens in open-hole completions in the wellbore is crucial for managing sand production. The main reason for using standalone screens in open-hole completions is their relatively reduced operational complexity compared to other sand control technologies. However, directly applying the screen to the bottom of the hole can lead to an incorrect screen type selection, resulting in an unreliable sand control method. To address this issue, a sand retention test is conducted to evaluate the performance of a standalone screen before field installation. Nevertheless, current sand retention test setups encounter several challenges. These include difficulties in identifying minimum retention requirements, interpreting results in the context of field conditions, and replicating field-specific parameters. The existing sand retention test introduces uncertainties, such as inaccurately replicating field requirements, inconsistent selection of wetting fluids, flow rates, and channel formation, leading to variations in the choice of the optimal screen using this test. In response to these challenges, this study aims to review the sand retention test and propose an improved sand retention method to overcome these problems. The focus of this article is to provide an in-depth analysis of previous sand retention test setups, their contributions to characterizing sand screens, and the parameters utilized in determining test outcomes. Additionally, this review outlines a procedure to investigate the impact of different particle sizes on screen erosion. Key findings emphasize the importance of using high-quality materials, proper screen design to resist damage and erosion, achieving acceptable natural packing behind the screen, and considering factors such as geology, wellbore conditions, and installation techniques. The analysis reveals that a high quantity of finer and poorly sorted sand increases sand production. The study recommends performing a sand pack test closer to reservoir conditions for better evaluation. Premium sand screens demonstrate the highest retention capacity, followed by metal mesh and wire-wrapped screens. Additionally, geotextiles show potential for enhancing sand retention, and screen design affects erosion resistance and service life.

Patella is an uncommon site for the occurrence of bone tumor. Benign tumors are more common as compared to malignant ones. Osteosarcoma of the patella is a very rare tumor with only a few cases reported in the literature. Here, we present a rare case of a small-cell variant of osteosarcoma of the patella. A 12-year-old boy presented to us with pain and swelling over his left knee for 6 months. On X-ray, expansion with osteosclerotic and osteolytic lesions was found in the patella and a suspected diagnosis of osteosarcoma was made. The patient was managed by excisional biopsy of the patella and V–Y plasty of the quadriceps tendon. Results of histopathological and immunohistochemistry analyses were consistent with osteosarcoma of small cell variant. The patient was referred to a higher center after the operation for radiotherapy and chemotherapy. On the final follow-up, after 1 year, the patient was found to have expired. Osteosarcoma of the patella is a rare condition. Further studies with a number of cases and longer duration of follow-up are warranted to obtain better insight that can aid in devising management protocol, prediction of survival, and functional outcome of different surgical interventions for these patients.

The spread of SARS-CoV-2 represented a global health crisis. On May 5, 2023, the chief of the World Health Organization (WHO) officially announced the end of COVID-19 as a global health emergency. Serological assays can identify previously infected SARS-CoV-2 individuals, even if they did not go for testing while acutely ill. The current study sought to detect antibodies directed against the nucleocapsid of SARS-CoV-2 (IgG against the SARS-CoV-2 N protein) in both vaccinated and unvaccinated COVID-19 individuals. Of the 100 participants, 53 and 47 were vaccinated and unvaccinated, respectively. The vaccination status of the cohort based on gender data indicates that 41 (41%) of all participants were vaccinated males, whereas 12 (12%) were vaccinated females. We found that 42 (42%) were unvaccinated males and 5 (5%) were unvaccinated females. Of 53 vaccinated subjects, 42 and 11 participants were positive and negative for IgG against the SARS-CoV-2 N protein, respectively. Of 47 unvaccinated participants, 28 and 19 were positive and negative for IgG against the SARS-CoV-2 N protein, respectively. The average of S/P “Sample/Positive control” percentages, which correlate to levels of IgG against SARS-CoV-2 N protein, were significantly higher among the vaccinated patients (73.8%) as compared to non-vaccinated patients (57.1%), with p = 0.02. There was a downward trend in levels of IgG against the SARS-CoV-2 N protein with increasing age, except for the 60–69 age group.

Introduction: Lentinula edodes (shiitake mushroom) has been integral to Traditional Chinese Medicine (TCM) for centuries, with its cultivation dating back to the Song dynasty (960–1127) in China. Traditionally valued for its immune-boosting properties, L. edodes is now being studied for its potential in managing metabolic disorders like obesity and type 2 diabetes. This study examines the effects of L. edodes fruiting body extract on key metabolic pathways related to glucose metabolism and lipid regulation. Methods: Fruiting body extracts of Lentinula edodes including water, ethanolic, methanolic, and hydroalcoholic (50:50, 75:25, 25:75) were tested on L6 myoblasts. The study evaluated glucose uptake, GLUT4 expression, p-AMPK activation, and gene expression levels of PPARγ, AMPK, UCP-1, and FASN. Comparisons were made with control groups and standards like berberine and insulin. Results: The hydroalcoholic extracts (50:50 and 75:25) significantly enhanced glucose uptake (p < 0.001) and GLUT4 translocation, similar to the effects of insulin (p < 0.0001) and berberine (p < 0.0001). These extracts also upregulated p-AMPK (p < 0.0001) and UCP-1 expression (7.621), indicating improved insulin sensitivity and thermogenic activity. Additionally, these extracts differentially regulated PPARγ and FASN, suggesting a complex interaction with lipid metabolism. Notably, FASN expression was highly upregulated in water extract (37.792), indicating potential implications for fatty acid synthesis and storage. Discussion: Lentinula edodes shows significant potential as a functional food for managing obesity and diabetes, supporting traditional TCM practices with modern scientific evidence. The study suggests that L. edodes can modulate key metabolic pathways such as glucose uptake, AMPK signalling, PPARγ regulation, and upregulation of FASN and UCP-1, making it a promising natural therapeutic agent. Further research, including clinical trials, is necessary to confirm these benefits in humans and explore the underlying mechanisms.

Additive manufacturing, also known as 3D printing, is an amazing innovation with a wide range of uses in intelligent agriculture and food processing. Along with adjustable farming equipment and autonomous agricultural instruments like drones and robots, it offers real-time data on plant health, nutrient levels, and soil state. 3D printing has reinvented food processing by enabling personalized nutrition solutions, particularly in the field of medicinal nutrition. It also makes it possible to alter the textures and structures of food, creating novel sensory experiences and better-quality goods. 3D printing contributes to sustainable food production by reducing food waste (10–30 %) and using alternative protein sources. According to the study, AI and 3D-assisted IoT sensors can help increase yield by 10 % to 15 % while significantly reducing crop deterioration. They can also help reduce water usage by 20 % to 25 %, labor requirements by 20 % to 30 %, and overall power consumption by 20 %. However, high costs, complex technical and design knowledge, and limitations on production speed and scale are obstacles to broader use. It's also necessary to handle safety and regulatory concerns. 3D printing has a promising future in various fields thanks to advancements in bioprinting, multifunctional materials, blockchain, and artificial intelligence integration. These advancements could boost 3D printing's potential and result in higher output, more sustainable practices, and higher-quality products.

In today’s digital world, app stores have become an essential part of software distribution, providing customers with a wide range of applications and opportunities for software developers to showcase their work. This study elaborates on the importance of end-user feedback for software evolution. However, in the literature, more emphasis has been given to high-rating & popular software apps while ignoring comparatively low-rating apps. Therefore, the proposed approach focuses on end-user reviews collected from 64 low-rated apps representing 14 categories in the Amazon App Store. We critically analyze feedback from low-rating apps and developed a grounded theory to identify various concepts important for software evolution and improving its quality including user interface (UI) and user experience (UX), functionality and features, compatibility and device-specific, performance and stability, customer support and responsiveness and security and privacy issues. Then, using a grounded theory and content analysis approach, a novel research dataset is curated to evaluate the performance of baseline machine learning (ML), and state-of-the-art deep learning (DL) algorithms in automatically classifying end-user feedback into frequently occurring issues. Various natural language processing and feature engineering techniques are utilized for improving and optimizing the performance of ML and DL classifiers. Also, an experimental study comparing various ML and DL algorithms, including multinomial naive Bayes (MNB), logistic regression (LR), random forest (RF), multi-layer perception (MLP), k-nearest neighbors (KNN), AdaBoost, Voting, convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short term memory (BiLSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (BiGRU), and recurrent neural network (RNN) classifiers, achieved satisfactory results in classifying end-user feedback to commonly occurring issues. Whereas, MLP, RF, BiGRU, GRU, CNN, LSTM, and Classifiers achieved average accuracies of 94%, 94%, 92%, 91%, 90%, 89%, and 89%, respectively. We employed the SHAP approach to identify the critical features associated with each issue type to enhance the explainability of the classifiers. This research sheds light on areas needing improvement in low-rated apps and opens up new avenues for developers to improve software quality based on user feedback.

Summary: Based on our hypothesis that myotubes exhibit a bistable response to insulin, here we present a protocol for finely measuring Akt phosphorylation in single myotubes under insulin stimulation. We describe steps to stably express a Förster resonance energy transfer (FRET)-based Akt biosensor in C2C12-derived myotubes and perform single-cell FRET imaging. This protocol highlights its potential for precision medicine in analyzing protein phosphorylation dynamics at the single-cell level.For complete details on the use and execution of this protocol, please refer to Akhtar et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

The complex and dynamic networks of river system, vital for the maintenance of biodiversity and ecosystem services, are under pressure due to human-induced water stress disrupting ecological processes. Recognition of the importance of rivers as source of life though has led to efforts towards protecting large rivers, nevertheless the conservation and management of smaller rivers remained mostly neglected, creating significant gaps in ecological restoration initiatives. In the present study, we assess the distribution and population status of Gangetic dolphin (Platanista gangetica) in the small rivers in the Ganga River Basin for integrating it into the basin wide river conservation strategy. We observed that the Gangetic dolphin inhabited most tributaries and sub-tributaries of the Ganga River, with a naïve occupancy rate of Ψ̂ = 0.68 ± 0.04 (mean ± SE) having 606 ± 142.77 (mean ± SE) individuals and accounts for 15% of the total Gangetic dolphin population in the Basin. The results of N-mixture and MaxEnt models demonstrate that channel depth, presence of meanders and water discharge were key predictors of distribution in these rivers, and the proximity to confluences were identified as a critical predictor. About 54% (620 km) of 1150 km of the surveyed river stretches exhibited suitability for dolphins in the Basin, indicated by a probability distribution score of ≥0.50. Combining this data with the available information, we delineated a total of 2850 km stretch covering entire Ganga River System suitable for Gangetic dolphins within the Basin. Notably, the 620 km of suitable stretches identified in smaller rivers represent 22% of the overall suitable stretches across the entire basin. These stretches were translated to conservation priority stretches for systematic conservation planning involving various stakeholders for improved river conservation in the Basin.

Anomaly detection in power systems is crucial for operational reliability and safety, often addressed through binary classification in existing research. However, a research gap exists in multiclass classification for enhanced reliability. To bridge this gap, this study employs four machine learning (ML) classifiers: Random Forest (RF), Decision Tree, Naive Bayes (NB), and Support Vector Machine (SVM) using comprehensive testing on a dataset comprising sixteen indices and their pair combinations (totaling 136 pairs). These classifiers, trained on a dataset derived from simulating a test system with hybrid DGs, exhibit superior anomaly detection, especially with the $\frac {dv}{dq}\& \frac {dv}{dp}$ pair. Among them, RF and DT classifier achieves precision, recall, and F score of unity and outperforming NB and SVM. The performance of the proposed RF and DT classifiers with $\frac {dv}{dq}\& \frac {dv}{dp}$ pair is compared with existing research papers in terms of accuracy and data division. The comparison shows that the proposed RF and DT classifiers with $\frac {dv}{dq}\& \frac {dv}{dp}$ pair achieve 100% accuracy even with 50% data division, whereas other techniques fail to achieve it even at 20% for testing and 80% for training. The study underscores the critical role of pair selection and classifier combinations in effective anomaly detection, facilitating the implementation of robust mitigating strategies for power system stability.

App stores allow users to search, download, and purchase software applications to accomplish daily tasks. Also, they enable crowd-users to submit textual feedback or star ratings to the downloaded software apps based on their satisfaction. Recently, crowd-user feedback contains critical information for software developers, including new features, issues, non-functional requirements, etc. Previously, identifying software bugs in low-star software applications was ignored in the literature. For this purpose, we proposed a natural language processing-based (NLP) approach to recover frequently occurring software issues in the Amazon Software App (ASA) store. The proposed approach identified prevalent issues using NLP part-of-speech (POS) analytics. Also, to better understand the implications of these issues on end-user satisfaction, different machine learning (ML) algorithms are used to identify crowd-user emotions such as anger, fear, sadness, and disgust with the identified issues. To this end, we shortlisted 45 software apps with comparatively low ratings from the ASA Store. We investigated how crowd-users reported their grudges and opinions against the software applications using the grounded theory & content analysis approaches and prepared a grounded truth for the ML experiments. ML algorithms, such as MNB, LR, RF, MLP, KNN, AdaBoost, and Voting Classifier, are used to identify the associated emotions with each captured issue by processing the annotated end-user data set. We obtained satisfactory classification results, with MLP and RF classifiers having 82% and 80% average accuracies, respectively. Furthermore, the ROC curves for better-performing ML classifiers are plotted to identify the best-performing under or oversampling classifier to be selected as the final best classifier. Based on our knowledge, the proposed approach is considered the first step in identifying frequently occurring issues and corresponding end-user emotions for low-ranked software applications. The software vendors can utilize the proposed approach to improve the performance of low-ranked software apps by incorporating it into the software evolution process promptly.

Abstract Etravirine (ERVN) is a potential NNRTI (non-nucleoside reverse transcriptase inhibitor) in treating HIV infection. It possesses extremely low oral bioavailability. The present research aims to optimize the formulation and characterization of TPGS-enriched ERVN-loaded lipid-based nanocarriers (NLCs) for HIV-infected patients. The formulation, ERVN–TPGS–NLCs, was optimized by central composite rotational design using a modified-solvent emulsification process. Various characterization parameters of NLCs were evaluated, including globule size of 121.56 ± 2.174 nm, PDI of 0.172 ± 0.042, the zeta potential of − 7.32 ± 0.021 mV, %EE of 94.42 ± 8.65% of ERVN and %DL was 8.94 ± 0.759% of ERVN and spherical shape was revealed by TEM. PXRD was also performed to identify the crystallinity of the sample. In-vitro drug release showed % a cumulative drug release of 83.72 ± 8.35% at pH 1.2 and 90.61 ± 9.11% at pH 6.8, respectively, whereas the % cumulative drug release from drug suspension (ERVN-S) was found to be 21.13 ± 2.01% at pH 1.2 and 24.84 ± 2.51 at pH 6.8 at the end of 48 h. Further, the intestinal permeation study and confocal microscope showed approximately three-fold and two-fold increased permeation in ERVN–TPGS–NLCs and ERVN-NLCs across the gut sac compared to ERVN-S. Hemolysis compatibility and lipolysis studies were performed to predict the in-vivo fate of the formulation. The pharmacokinetic study revealed a 3.13-fold increment in the relative bioavailability, which agrees with the ex-vivo studies, and lymphatic uptake was validated by using cycloheximide along with designed formulation, which showed the impact of lymphatic uptake in AUC. This study ensures that ERVN–TPGS–NLCs take lymphatic uptake to minimize the first-pass metabolism followed by improved oral bioavailability of ERVN. Thus, the enhanced bioavailability of ERVN can reduce the high dose of ERVN to minimize the adverse effects related to dose-related burden. Graphical abstract

Viral and non-viral hepatocellular carcinoma (HCC) is becoming predominant in developing countries. A major issue linked to HCC-related mortality rate is the late diagnosis of cancer development. Although traditional approaches to diagnosing HCC have become gold-standard, there remain several limitations due to which the confirmation of cancer progression takes a longer period. The recent emergence of artificial intelligence tools with the capacity to analyze biomedical datasets is assisting traditional diagnostic approaches for early diagnosis with certainty. Here we present a review of traditional HCC diagnostic approaches versus the use of artificial intelligence (Machine Learning and Deep Learning) for HCC diagnosis. The overview of the cancer-related databases along with the use of AI in histopathology, radiology, biomarker, and electronic health records (EHRs) based HCC diagnosis is given.

High-gain DC-DC converters are becoming increasingly popular in renewable energy applications and solar PV systems. This article introduces a non-isolated non-coupled inductor-based high-gain DC-DC boost converter with the desirable features of low voltage stress on controlled power switches and high voltage gain at lower duty ratios. The proposed converter is well suited for boosting the low-input DC voltage obtained from distributed generation units like photovoltaic (PV) or fuel cells to substantially higher DC voltage. The converter comprises only two switches, and a single PWM signal governs its operation. These characteristics result in a topology that is more compact, less costly, lighter weight, and easier to control. The proposed converter is compared with some previously developed topologies of high-gain boost converters across various performance parameters. This comparison illustrates that the proposed model surpasses them in every aspect. A 300 W hardware prototype of a proposed model is developed and tested in a laboratory environment to confirm the theoretical assertions of a proposed model. The proposed topology offers a promising solution and enables a high gain of approximately 12 times the input voltage at a smaller duty ratio, specifically 11.25 at a duty of 0.6 and 17.77 at a duty of 0.7. The efficiency of a proposed converter ranges between 92.5% to 94.5% making it appropriate for numerous medium-high power applications. The uniqueness of the suggested system is its simple design and greater efficiency, which provides a more considerable gain and higher output voltages for sustainable energy systems.

Intraosseous ganglion is a benign cystic bony lesion containing viscous and mucinous gelatinous material, which is usually seen in middle-aged individuals. It is similar to soft-tissue ganglion cysts present in other parts of the body. We, hereby, report a rare case of idiopathic intraosseous ganglion of the tibia in a young adult who presented with chronic pain in his lower leg. The patient was subjected to X-ray and MRI investigations and managed by excisional curettage of the lesion and histopathological examination. On histopathology, the diagnosis of intraosseous ganglion was confirmed. On 2-year follow-up, the patient was doing well without any complication and recurrence. This report describes the diagnosis and successful management of intraosseous ganglion at a very rare site in a young individual. More in-depth studies are required to understand the predisposing factors, underlying pathology, early diagnosis, and management of this lesion.

A family of chemical compounds known as metal–organic networks (MONs) is composed mainly of clusters of metal ions with organic ligands. It can increase volatility or make substances soluble in organic solvents. By using these salient features, organic compounds generate applications in material sciences for sol–gel processing. A graph’s entropy is utilized as a complexity indicator and is interpreted as the structural information content of the graph. Investigating the entropies of relationship systems is a common occurrence in discrete mathematics, computer science, information theory, statistics, chemistry, and biology. In this article, we investigated the degree-based entropies: geometric arithmetic entropy, atom bond connectivity entropy, general Randic′ entropy, and general sum connectivity entropy for MONs. Furthermore, we created tables for all expressions by using 1–10 values for the ss parameter of these entropies.

Root-knot nematodes (Meloidogyne spp.) are sedentary endo-parasite that causes severe yield loss in carrot. Chemical nematicides currently used to manage Meloidogyne incognita are being phased out because of rising health and environmental issues. This study aimed to evaluate nematicidal effect of various concentrations, viz., 250, 500, 750, 1000ppm of leaf extract of Ipomoea carnea against M. incognita infecting carrot under in vitro and in pots assays. In our result, all tested concentrations displayed J2s mortality and egg hatching inhibition along with improving growth of carrot and reduced J2s population and root-knot index. Molecular docking performed predicts binding interactions of two major compounds, viz., neophytadiene and 2-amino-2-methyl-1-propanol as shown by GC-MS analysis with targeted protein, odorant response gene-1 of M. incognita, to confirm nematicidal action of I. carnea leaf extract. The obtained results also suggested that neophytadiene interacted more and strongly bound with odorant response gene-3 than 2-amino-2-methyl-1-propanol. The biochemical ligand-target protein interaction described in the present work will be helpful in the logical selection of biomolecules and essential proteins. Therefore, plant extract may be used the best alternative to chemical nematicides to control root-knot nematodes and caused longitudinal growth of the plant as well as reduce environmental risks.

Mucus plugging of the respiratory tract occurs in airway diseases, including asthma, chronic obstructive pulmonary disease, and cystic fibrosis. It can cause blockage of the airways, leading to breathlessness and lung failure. Here, we used a ventilatory setup to demonstrate the effect of BromAc® in dissolving mucus plugs in a novel ex vivo ovine obstructive lung model. Mucus simulant was filled into the trachea of freshly slaughtered ovine lungs and ventilated via an endotracheal tube (ETT) using Continuous Mandatory Ventilation. Predetermined single or repeated doses of Bromelain, Acetylcysteine (Ac), BromAc®, and saline control were administered via an Aerogen® vibrating nebulizer and ventilated for 30 or 60 min. Ventilatory recording of resistance, compliance, and tidal volume was conducted, and rheology pre- and post-treatment were measured. A significant decline in airway resistance (p < 0.0001) compared to the saline control was observed when treated with Bromelain, Ac, and BromAc®, with the latter showing a stronger mucolytic effect than single agents. The decline in resistance was also effective in shorter time points (p < 0.05) at lower doses of the drugs. Changes in compliance, peak pressure, and tidal volume were not observed after administration of the drugs. Rheology measurements revealed that BromAc®TM significantly reduced the viscosity of the mucin at the end of 30 min and 60 min time points (p < 0.001) compared to the saline control. BromAc® showed complete dissolution of the respiratory mucus simulant and improved ventilatory airflow parameters in the ex vivo ovine model.

Background: Medical students are confronted with substantial academic and psychological stressors during their education. Studies report that examination stress can have a noteworthy effect on the leukocyte count of students. This study aimed to evaluate the physiological effects of examination stress on the total and differential leucocyte cell count in medical students at Peoples University of Medical and Health Sciences for Women, Nawabshah. Methods: A cross-sectional comparative study was conducted from April to October 2019, with 110 MBBS students, divided into two groups of 55 students using a non-probability randomized sampling technique with first-year and second-year female students, whereas hypertensive, diabetic, and with any psychiatric disease were excluded. Blood samples were taken before and during the examination. Data were analyzed by SPSS version 23. All the continuous variables were analyzed by the t-test for Mean±S.D while all the numerical variables were analyzed by Student’s t-test. Results: Pre-exam complete blood count (CBC) test reports of first-year students showed Neutrophils, Lymphocytes, Eosinophil, and Monocytes cell counts of 69.2182, 28.5455, 1.61, 1.2545 cell /microliter whereas complete blood count during exams showed 69.909, 27.9636, 1.41and 1.2000 cells/ microliter. In the second-year student, complete blood count before and during the exam showed Neutrophils 66.5273 to 64.0182, Lymphocytes 31.236 to 30.036, Eosinophil 1.92 to 1.76, and Monocytes 1.473 to 1.364 cells/microliter, the decreased values showing that there is an influence of exam stress on white blood cells. Conclusion: Exams in medical schools can be stressful enough to induce a substantial decrease in blood cell parameters among medical students.

The graphical user interface (GUI) in mobile applications plays a crucial role in connecting users with mobile applications. GUIs often receive many UI design smells, bugs, or feature enhancement requests. The design smells include text overlap, component occlusion, blur screens, null values, and missing images. It also provides for the behavior of mobile applications during their usage. Manual testing of mobile applications (app as short in the rest of the document) is essential to ensuring app quality, especially for identifying usability and accessibility that may be missed during automated testing. However, it is time-consuming and inefficient due to the need for testers to perform actions repeatedly and the possibility of missing some functionalities. Although several approaches have been proposed, they require significant performance improvement. In addition, the key challenges of these approaches are incorporating the design guidelines and rules necessary to follow during app development and combine the syntactical and semantic information available on the development forums. In this study, we proposed a UI bug identification and localization approach called Mobile-UI-Repair (M-UI-R). M-UI-R is capable of recognizing graphical user interfaces (GUIs) display issues and accurately identifying the specific location of the bug within the GUI. M-UI-R is trained and tested on the history data and also validated on real-time data. The evaluation shows that the average precision is 87.7% and the average recall is 86.5% achieved in the detection of UI display issues. M-UI-R also achieved an average precision of 71.5% and an average recall of 70.7% in the localization of UI design smell. Moreover, a survey involving eight developers demonstrates that the proposed approach provides valuable support for enhancing the user interface of mobile applications. This aids developers in their efforts to fix bugs.

Abstract Objectives The study aimed to assess the effectiveness of anxiety reduction protocol using auditory distraction in alleviating dental anxiety among patients undergoing tooth extraction. Materials and Methods A randomized controlled trial was conducted at the Oral Surgery Department at Islamabad Dental Hospital from July to December 2022, involving 50 patients scheduled for tooth extraction. Participants were randomly divided into two groups: an interventional group, exposed to auditory distraction, and a noninterventional group, without exposure to auditory distraction before the dental extraction. Dental anxiety was measured using the modified dental anxiety scale (MDAS) questionnaire, which scores anxiety levels on a range from 5 (not anxious) to 25 (extremely anxious). Anxiety levels were assessed in the waiting room and just before extraction, and the results were compared across both groups to evaluate the effectiveness of auditory distraction in reducing dental anxiety. Results The sample size of 50 was randomly and equally allocated to the interventional and noninterventional groups. The study population consisted of 28 (56%) female and 22 (44%) male participants. No significant difference was observed between the anxiety scores of interventional and noninterventional groups at baseline. A significant reduction in anxiety scores was observed in the intervention group during postintervention assessment, while no significant difference was seen in the noninterventional group's anxiety scores. Conclusions The study supports the efficacy of anxiety reduction protocol using auditory distraction as a practical tool for reducing dental anxiety among patients undergoing tooth extraction.

Emotion detection (ED) involves the identification and understanding of an individual’s emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person’s emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

Abstract Small and Medium Enterprises (SMEs) are steadily moving in the direction of implementing digital and smart technologies, including the Industrial Internet of Things (IIoT) for improving their products and services. The adoption of IIoT allows manufactures and producers to make quick decisions for improving productivity and quality in real‐time. For this purpose, the era of digital industrial revolution from IR 1.0 to IR 5.0 is briefly explained. In this research study, the authors have reviewed and analysed the existing reviews, surveys and technical research studies on IIoT technologies for the manufacturing and production SMEs to highlight the concern raised. Forty‐seven (47) influencing factors are identified and classified into four groups based on the TOEI framework. Based on the identified influencing factors, IIoT adoption model is proposed for the manufacturing and production SMEs to adopt the new IIoT technologies in their business environments. Furthermore, a comparative analysis of the influencing factors has been done for the adoption of IIoT to increase efficiency, productivity and competitiveness for the manufacturing and production SMEs in developing countries. The proposed IIoT adoption model will help future policymakers and stakeholders to develop policies and strategies for the successful adoption and implementation of IIoT in manufacturing and production SMEs in developing countries. Also, recommendations are suggested to encourage IIoT adoption in production and manufacturing environments so that manufacturers and producers can respond easily and quickly to highly changing demands, product trends, skills gaps and other unexpected challenges in the future.

Conventional modes of space heating are expensive and cause greenhouse gas emissions resulting in global warming. In this paper, an experimental analysis of a hybrid solar space heating system consisting of flat plate collectors (FPC) and evacuated tube collectors (ETC) along with a convective radiator is presented. Techno-economic and environmental analysis of the system is performed under real winter conditions in a subtropical climate. The experimental setup is designed such that flat plate collectors and evacuated tube collectors can also be operated separately for comparative analysis with the hybrid system (HC). The experimental results show that ETC achieved a maximum average energy efficiency of 72 % followed by the hybrid collector (HC) and FPC with energy efficiencies of 61 % and 57 %, respectively. The maximum exergy efficiency for ETC, HC and FPC are 54 %, 42 % and 33 % respectively in the month of December with low ambient temperatures. The corresponding maximum sustainability index achieved by ETC, HC and FPC, is 1.76, 1.52 and 1.39 respectively. In addition, the panel radiator achieved a maximum heat gain of 1484 W with an average room temperature in the range of 16.4–24.3 ℃. The economic analysis shows that the benefit-to-cost ratio for the hybrid varies from 0.82 to 1.6 and the payback period varies from 8.9 to 7.41 years compared to the electric heater for an inflation rate of 6–13 %. About 38 % more life cycle cost is observed for electric heater as compared to HSSHS. Environmental analysis reveals that 99 % and 78 % more greenhouse gas emissions are caused by conventional heaters compared to hybrid systems. Thus, a hybrid arrangement of FPC and ETC can be more beneficial for space heating compared to individual installations of these low-temperature solar collectors.

Abstract Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy.

Conclusion: Our study documents three circulating serotypes in the capital territory of Pakistan and highlights that the SVM outperformed other models, potentially serving as a valuable tool in clinical settings to aid in the rapid diagnosis of DF.

Background and objectives: Pre-eclampsia (PE) is a pregnancy-specific condition characterized by significant health risks for pregnant women worldwide due to its status as a multi-organ disorder. High blood pressure (hypertension) with or without proteinuria is usually considered an initial clinical sign of PE. The pathogenesis of pre-eclampsia is highly complex and likely involves multiple factors, including poorly developed uterine spiral arterioles, immunological issues, placental ischemia or infarction, and genetic abnormalities. Inflammatory cytokine production, regulated by cytokine gene polymorphisms, is one of the factors likely contributing to the development of PE. The present study aimed to assess IL-6, IL-1β, and Apo B-100 gene polymorphism and to evaluate the association of these polymorphisms with PE. Materials and Methods: This cross-sectional observational study involved 99 participants aged 16 to 45 years from Bahawal Victoria Hospital Bahawalpur, Punjab, Pakistan. The participants were divided into three groups: Group 1 (PE with severe hypertension), Group 2 (PE with hypertension), and Group 3 (control), each comprising 33 individuals. Maternal blood samples were collected, DNA was extracted, and molecular genetic analysis of the IL-6, IL-1β, and Apo B-100 genes was performed using the PCR-RFLP method. Allelic frequencies were compared, and statistical analysis was conducted using SPSS 25, applying the Hardy–Weinberg equation and chi-square test to evaluate the results. Results: There are differences in the distribution of allelic frequencies for IL-6 -174G/C (CC, GC, GG), IL-1β-511C/T (CC, CT, TT), and Apo B-100 2488 C/T (CC, CT, TT) between pre-eclamptic patients and the control group. The analysis using the Hardy–Weinberg equilibrium and chi-square test showed an association between the IL-6-174 G/C polymorphism and the severity of pre-eclampsia. Conclusions: The polymorphisms of the IL-6, IL-1β, and Apo B-100 genes revealed different alleles. The IL-6 gene alone was found to be in disequilibrium according to the Hardy–Weinberg equation, indicating a potential link to the severity of pre-eclampsia in the population studied.

Dengue fever, caused by the dengue virus (DENV), poses a significant global health threat, with a dramatic increase in cases driven by climate change, urbanization, and mosquito resistance. In Pakistan, a country with a population of 240 million, the world’s fifth largest, dengue has emerged as an escalating public health crisis, with seasonal outbreaks severely straining the healthcare system. Despite decades of vector control efforts, there has not been much success, necessitating the introduction of dengue vaccination to boost population immunity. Recent advancements in vaccine development demonstrate promising efficacy and safety profiles, even in dengue-naive individuals. Implementing a dengue vaccination program in Pakistan could significantly reduce the disease burden, lower healthcare costs, and prevent future outbreaks. Integrating vaccination with existing public health initiatives can achieve high coverage and improve overall public health outcomes.

Background: Apolipoprotein E (APOE) gene polymorphism has been implicated in the pathogenesis of various metabolic disorders, including type 2 diabetes mellitus (T2DM). Type 2 diabetes mellitus (T2DM) is a major public health concern worldwide, including in Pakistan. Cardiovascular problems linked with T2DM have a significant impact on individuals and society. The goal of this study is to investigate the relationship between Apolipoprotein E (ApoE) genotypes, dyslipidemia, and cardiovascular complications such as ischemic heart disease (IHD) and stroke. Methods: This study was carried out on 260 subjects divided into controls and diabetics. The diabetics were further divided into four subgroups such as D1: diabetics without cardiovascular issues, D2: diabetics with heart disease, D3: diabetics with stroke, and D4: diabetics with both heart disease and stroke. Anthropometric parameters (age, BMI) and risk factors (smoking, diabetes duration, hypertension) were assessed in all groups. Serum levels of TC, TG, LDL, HDL, VLDL, creatinine, BSF, and HbA1c were also measured. Apolipoprotein E gene polymorphism was determined using PCR-RFLP. Results: Hypertension, BMI, and dyslipidemia are defined as elevated levels of total cholesterol, triglycerides, LDL, and VLDL, and decreased levels of HDL. Uncontrolled hyperglycemia (elevated fasting blood sugar and glycated hemoglobin) in T2DM was linked to vascular complications such as IHD and stroke. Hypertension was prevalent in 79.3% of the population. Stage 2 hypertension was more prevalent in all age groups. It was also noted that common genotypes in the Pakistani population are 3/3, 4/4, 2/3, and 3/4. The frequency of genotypes 3/4 and 2/3 is highest in diabetics with stroke. Genotype 3/3 is present frequently in diabetics with IHD/stroke and patients with both these complications. However, genotype 4/4 is most frequently found in diabetics with IHD. Conclusions: It is concluded that BMI, hypertension, hyperglycemia, atherosclerosis, and dyslipidemia are linked with cardiovascular complications of type 2 diabetes. Apolipoprotein E gene polymorphism is associated with cardiovascular disease in patients with diabetes by affecting the lipid profile.

James F Donohue,1 J Stuart Elborn,2 Peter Lansberg,3 Afzal Javed,4 Solomon Tesfaye,5 Hope Rugo,6 Sita Ratna Devi Duddi,7,8 Niraksha Jithoo,9 Pai-Hui Huang,10 Kannan Subramaniam,11 Nagendra Ramanjinappa,12 Arkady Koltun,13 Shari Melamed,13 Juliana CN Chan14 1UNC School of Medicine, Chapel Hill, NC, USA; 2Queen’s University, Belfast, UK; 3University Medical Center, Groningen, the Netherlands; 4Warwick Medical School, University of Warwick, Warwick, UK & Pakistan Psychiatric Research Centre, Coventry, UK; 5Sheffield Teaching Hospitals and the University of Sheffield, Sheffield, UK; 6University of California San Francisco Comprehensive Cancer Center, San Francisco, CA, USA; 7International Alliance of Patients’ Organisations, London, United Kingdom; 8DakshamA Health and Education, Delhi, India; 9Viatris Ltd, Johannesburg, South Africa; 10Viatris Pharmaceutical Co., Ltd, Taipei, Taiwan; 11Viatris Ltd, Auckland, New Zealand; 12Viatris Ltd, Bangalore, India; 13Viatris Ltd, Canonsburg, PA, USA; 14Department of Medicine and Therapeutics, Hong Kong Institute of Diabetes and Obesity, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, Special Administrative Regions of the People’s Republic of ChinaCorrespondence: Juliana CN Chan, Department of Medicine and Therapeutics, Hong Kong Institute of Diabetes and Obesity, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, Special Administrative Regions of the People’s Republic of China, Tel +852 3505 3138, Email jchan@cuhk.edu.hkAbstract: According to the United Nations High-Level Meeting 2018, five non-communicable diseases (NCDs) including cardiovascular diseases, chronic respiratory diseases, diabetes mellitus, cancer, and mental health conditions accounted for two-thirds of global deaths. These five NCDs share five common risk factors including tobacco use, unhealthy diets, physical inactivity, alcohol use, and air pollution. Low- and middle-income countries (LMICs) face larger burden of NCDs than high-income countries (HICs), due to differences in ecological, technological, socioeconomic and health system development. Based on high-level evidence albeit mainly from HICs, the burden caused by NCDs can be reduced by affordable medicines and best practices. However, “know-do” gaps, ie, gaps between what we know in science and what we do in practice, has limited the impact of these strategies, especially in LMICs. Implementation science advocates the use of robust methodologies to evaluate sustainable solutions in health, education and social care aimed at informing practice and policies. In this article, physician researchers with expertise in NCDs reviewed the common challenges shared by these five NCDs with different clinical courses. They explained the principles of implementation science and advocated the use of an evidence-based framework to implement solutions focusing on early detection, prevention and empowerment, supplemented by best practices in HICs and LMICs. These successful stories can be used to motivate policymakers, payors, providers, patients and public to co-design frameworks and implement context-relevant, multi-component, evidence-based practices. In pursuit of this goal, we propose partnership, leadership, and access to continuing care as the three pillars in developing roadmaps for addressing the multiple needs during the journey of a person with or at risk of these five NCDs. By transforming the ecosystem, raising awareness and aligning context-relevant practices and policies with ongoing evaluation, it is possible to make healthcare accessible, affordable and sustainable to reduce the burden of these five NCDs.Keywords: non-communicable diseases, know-do gaps, implementation science, evidence-based practices, healthcare policies

The unproductive time is very high due to drill string jamming. So the main objective of this research is to determine the influences of the parameters in the accidents of stuck pipes using the construction of XGBoost models. To develop the model, drilling parameters are taken from daily drilling reports of the construction of 30 wells in the West Qurna field, Iraq. The data includes well drilling reports from 2013 to 2020. The results show that the factors such as Measured depth (MD), Rate of penetration (ROP), 10-sec gel (GEL1), Plastic Viscosity (PV), Mud weight (MW), Yield point (YP) contribute positively to the model predictions. In contrast, the factors such as Flow rate (FR), Rotation per minute (RPM), Filtrate (API/HPHT-FILTR and Bottom hole assembly (BHA) length has a negative contribution to the final model predictions. This research concluded that pipe sticking in the borehole is primarily due to inclination, penetration rate, and flow rate. This study is useful in the drilling of any field.

Many studies have investigated the antiviral activity of cytokines, including interleukin-6 (IL-6), interleukin-22 (IL-22), interleukin-32 gamma (IL-32γ), and interferon-lambda (IFN-λ) in diverse populations. This study aims to evaluate the role of these cytokines in inhibition of various human and animal viruses when administered exogenously. A comprehensive meta-analysis and systematic review were conducted on all the relevant studies from three databases. Standard mean differences (SMDs) of overall viral inhibition were used to generate the difference in the antiviral efficacy of these cytokines between control and experimental groups. A total of 4,618 abstracts for IL-6, 3,517 abstracts for IL-22, 2,160 abstracts for IL-32γ, and 1,026 abstracts for IFN-λ were identified, and 7, 4, 8, and 35 studies were included, respectively, for each cytokine. IFN-λ (SMD = 0.9540; 95% CI: 0.69–0.22) and IL-32γ (SMD = 0.459; 95% CI: 0.02–0.90) showed the highest influence followed by IL-6 (SMD = 0.456; CI: −0.04–0.95) and IL-22 (SMD = 0.244; 95% CI: −0.33–0.81). None of the cytokines represented heterogeneity (tau² > 0), but only IFN-λ indicated the funnel plot asymmetry (p = 0.0097). Results also indicated that IFN-λ and IL-32γ are more potent antivirals than IL-6 and IL-22. The collective findings of this study emphasize that exogenously administered pro-inflammatory cytokines, specifically IFN-λ and IL-32, exhibit a significant antiviral activity, thereby underscoring them as potent antiviral agents. Nonetheless, additional research is required to ascertain their clinical utility and potential for integration into combinatorial therapeutic regimens against viral infections.

Phishing constitutes a form of social engineering that aims to deceive individuals through email communication. Extensive prior research has underscored phishing as one of the most commonly employed attack vectors for infiltrating organizational networks. A prevalent method involves misleading the target by employing phishing URLs concealed through hyperlink strategies. PhishTank, a website employing the concept of crowd-sourcing, aggregates phishing URLs and subsequently verifies their authenticity. In the course of this study, we leveraged a Python script to extract data from the PhishTank website, amassing a comprehensive dataset comprising over 190,0000 phishing URLs. This dataset is a valuable resource that can be harnessed by both researchers and practitioners for enhancing phish- ing filters, fortifying firewalls, security education, and refining training and testing models, among other applications.

Foam flooding by Foam Assisted Water-Alternating-Gas (FAWAG) is an important enhanced oil recovery method that has proven successful in experimental and pilot studies. The present study is carried out to monitor the movement of the foam front once injected into the porous medium. This study aims to investigate applications of resistivity waves to monitor foam propagation in a sandstone formation. In the present lab-scale experiments and simulations, resistivity measurements were carried out to monitor the progression of foam in a sand pack, and the relationships between foam injection time and resistivity, as well as brine saturation, were studied. The brine saturation from foam simulation using CMG STAR is exported to COMSOL and calculated true formation resistivity. A diagram was produced summarizing the progression of foam through the sand pack in the function of time, which enabled us to establish how foam progressed through a porous medium. A surfactant and brine mixture was injected into the sand pack, followed by nitrogen gas to generate the foam in situ. As foam progressed through the sand pack, resistance measurements were taken in three zones of the sand pack. The resistance was then converted into resistivity and finally into brine saturation. As foam travels through the sand pack, it is predicted to displace the brine initially in place. This gradually increases each zone's resistivity (decreases the brine saturation) by displacing the brine. Also, an increase in the surfactant concentration results in higher resistivity. Finally, a comparison of three different surfactant concentrations was evaluated in terms of resistivity results, water saturation, and foam propagation monitoring to obtain the optimum surfactant concentration involved in foam flooding.

The nanoparticles of Zinc ferrite have been broadly used in various fields such as solar cells, photocatalysis, hydrogen storage, sensors etc. Here, the zinc ferrite nanoparticles (ZnFe2O4NPs) was prepared via solution process and characterized in detail with instruments such as XRD, FESEM, TEM, FTIR and TGA. The characterization studies revealed that the average individual size of each NP is ∼ 27 ± 1 nm size. The FTIR shows the functional band of metal and oxygen was obtained at 570 cm−1. Although, the processing and use of zinc iron oxide based nanostructures are very widely utilized in industries but a limited studies are available towards the biological studies. The current work explores the application of ZnFe2O4NPs with the concentration range from 2 to 200 μg/mL against breast cancer cells (MCF-7) to examine the cytotoxicity of oxidative stress and morphological variations using MTT, NRU, LPO, GSH assays after 24 h of treatment. Dose dependent drop in cell survival was seen in MTT and NRU assays. A significant increase in LPO was observed in MCF-7 cells exposed to ZnFe2O4NPs after 24 h of treatment. However, the GSH levels in MCF-7 cells exposed to ZnFe2O4NPs decreased significantly after 24 h. Further, the study of apoptotic gene expression was seen by real time PCR analysis was also conducted and described.