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Carissa carandas L. is a wild food plant, however, is cultivated for its fruits to be utilized to make pickles, jams, and other similar products. Fruit of this plant has been intensively investigated for its bioactive potential; however, a scanty study is reported on the leaf, which is usually wasted. In the present study, ethanolic extract of the leaf of C. carandas was analyzed for its total bioactive contents and found rich in phenolics and flavonoids (26.67 ± 0.33 mg GAE/g extract and 18.42 ± 0.05 mg RE/g extract, respectively). These findings were substantiated due to UHPLCMS/MS analysis, which disclosed the presence of about 110 secondary metabolites, mostly lignans, flavonoids, and terpenoids. In DPPH and ABTS free radical inhibition assays, the extract showed inhibitory values as 47.51 ± 0.09 and 93.09 ± 2.8 mg TE/g extract, respectively. Its further antioxidant potential was observed in CUPRAC and FRAP assays, wherein the metal reducing power was recorded as 102.05 ± 1.03 and 67.24 ± 0.9 mg TE/g extract, respectively, while in phosphomolybdenum and metal chelating assays, the extract also disclosed potential activity (0.92 ± 0.02 mmol TE/g extract and 21.42 ± 0.34 mg EDTAE/g extract). The extract was also significantly active against tyrosinase (45.69 ± 0.7 mg KAE/g extract) and showed mild activities against AChE (3.61 ± 0.16 mg GALAE/g extract), BChE (1.43 ± 0.2 mg GALAE/g extract), α-amylase (0.46 ± 0.01 mmol ACAE/g extract) and α-glucosidase (1.05 ± 0.02 mmol ACAE/g extract). In computational studies, binding affinities for AChE and BChE enzymes were from −4.21 to −12.70 and −7.58 to −12.53 kcal/mol, respectively, confirming the results of the invitro tests and ADME studies. The current finding suggested that C. carandas may be considered as a source for several nutraceutical formulations.

This study explored the nutritional qualities of black mash bean flour impacted by fermentation, gelatinization and their combination. Protease activity, in vitro protein digestibility (IVPD), and protein solubility were evaluated for both fermented and unfermented black mash bean flour samples. Interestingly, when compared to fermented samples and gelatinized mash bean flour (GMBF), raw mash bean flour (RMBF) showed the highest protein solubility. Due to enzymatic activity and protein hydrolysis, fermentation with Lactobacillus E14 and Saccharomyces cerevisiae MK-157 enhanced protein solubility. Prolonged fermentation timeframes were positively correlated with protease activity and IVPD, suggesting the degradation of complex proteins. The highest levels of IVPD (90.12%) and protease activity (5546.78 U/g) were detected in GMBF fermented by S. cerevisiae MK-157, indicating the impact of fermentation on protein breakdown. The Lactobacillus E14-fermented RMBF exhibited 5521.45 U/g of protease activity and 87.54% IVPD. The mineral content of the fermented samples was substantially greater than that of the unfermented GMBF. The increased calcium, magnesium, potassium, sodium, iron, zinc, and copper levels for the GMBF fermented by S. cerevisiae MK-157 indicated that fermentation facilitated the release of minerals from chelated compounds. Microscopic examination revealed higher protein quantities in the fermented flour as well as hydrolyzed and broken starch granules embedded in the protein matrix. Large mash bean proteins were shown to be proteolyzed during fermentation into smaller peptides by SDS PAGE. Storage proteins were extensively broken down during fermentation with S. cerevisiae MK-157, whereas Lactobacillus E14 displayed a more focused pattern of hydrolysis. The nutritional profile of black mash bean flour was improved by fermentation with Lactobacillus E14 and S. cerevisiae MK-157.

Abstract Background There are avoidable differences (i.e., inequities) in the prevalence and distribution of chronic pain across diverse populations, as well as in access to and outcomes of pain management services. Digital pain self-reporting tools have the potential to reduce or exacerbate these inequities. This study aimed to better understand how to optimise the health equity impact of digital pain self-reporting tools on people who are experiencing (or are at risk of) digital pain inequities. Methods This was a qualitative study, guided by the Health Equity Impact Assessment tool—digital health supplement (HEIA-DH). We conducted three scoping focus groups with multiple stakeholders to identify the potential impacts of digital pain self-reporting tools and strategies to manage these impacts. Each group focused on one priority group experiencing digital pain inequities, including older adults, ethnic minorities, and people living in socio-economically deprived areas. A fourth consensus focus group was organised to discuss and select impact management strategies. Focus groups were audio-recorded, transcribed verbatim, and analysed using a framework approach. We derived codes, grouped them under four pre-defined categories from the HEIA-DH, and illustrated them with participants’ quotes. Results A total of fifteen people living with musculoskeletal pain conditions and thirteen professionals took part. Participants described how digital pain self-reports can have a positive health equity impact by better capturing pain fluctuations and enriching patient-provider communication, which in turn can enhance clinical decisions and self-management practices. Conversely, participants identified that incorrect interpretation of pain reports, lack of knowledge of pain terminologies, and digital (e.g., no access to technology) and social (e.g., gender stereotyping) exclusions may negatively impact on people’s health equity. The participants identified 32 strategies, of which 20 were selected as being likely to mitigate these negative health equity impacts. Example strategies included, e.g., option to customise self-reporting tools in line with users’ personal preferences, or resources to better explain how self-reported pain data will be used to build trust. Conclusion Linked to people’s personal and social characteristics, there are equity-based considerations for developing accessible digital pain self-reporting tools, as well as resources and skills to enable the adoption and use of these tools among priority groups. Future research should focus on implementing these equity-based considerations or strategies identified by our study and monitoring their impact on the health equity of people living with chronic pain.

Abstract The widespread use of oxidative hair colouring cosmetics threatens public health. Phenylenediamine derivatives serve as the main pigment in permanent hair colours. They interact with biological macromolecules, altering their functional and structural physiology. The study aimed to investigate the effect of a typical synthetic hair dye pigment, 4-Chloro-orthophenylenediamine (4-Cl-OPD), under a nitrating environment of DEANO on the calf thymus DNA molecule. The results showed single-stranded regions, base/sugar-phosphate backbone alterations, molecular changes, and nitro-oxidative lesions. These modifications are referred to as neo-epitopes on the DNA molecule. IgGs from cancer patients with a history of permanent hair dye use were screened for the recognition of neo-epitopes on DNA molecules. Hepatocellular carcinoma IgG showed the highest binding with 56% inhibition in the competition ELISA. The immune complex formation was observed through electrophoretic mobility shift assay. In conclusion, synthetic hair dye users are likely to present with heightened immunological triggers under elevated nitric oxide levels. The study reports chronic hair dye exposure as one of the factors responsible for altering the intricacies of the DNA’s microarchitectural structure and inducing neo-epitopes on the molecule. The physiological status of NO may define the susceptibility towards 4-Cl-OPD and humoral response in hair dye users. Persistent nitro-oxidative stress due to 4-Cl-OPD and NO may induce a heightened immune response against neoepitopes in the nitro-oxidatively modified DNA. Therefore, chronic hair dye exposure may be identified as a risk to human health. These findings may contribute to a better understanding and reinforcement of hair dye as one of the modifiable risk factors responsible for the pro-inflammatory carcinogenic environment.

Abstract This research investigates the impact of the technologies through an integrated Fuzzy Analytic Hierarchy Process (F-AHP) and VIKOR model to determine their viability as a marketing strategy for business growth in the Prosthetics and Orthotics (P&O) medical device industry. Based on expert data, the study evaluates the integration of Industry 4.0 technologies and ranks them by priority for marketing performance: “Artificial Intelligence & Machine Learning” (0.3295), “Internet of Things” (0.4325), “Virtual Reality & Augmented Reality” (0.5000), and “Big Data” (0.5704). The findings confirm that the proposed model effectively Leverage the industry 4.0 technologies to enhance marketing strategies, using Pakistan as a case study. This research serves as a blueprint for addressing the integration of next-generation marketing tools, technological advancements, and business growth challenges in the P&O industry. It contributes to the growing body of knowledge on the role of technology in improving healthcare marketing and business performance in emerging markets. Future research should explore the broader impact of Industry 4.0 technologies on healthcare marketing, focusing on strategic decision-making, efficient marketing resource allocation, and performance measurement to ensure sustained success in the P&O medical device sector. The study concludes that prioritizing Industry 4.0 technologies, however, limitations such as sample size, data collection constraints, and model complexity may Leverage the generalizability of the results. Future research should address these limitations by expanding the scope of expert involvement and exploring the application of this model in other emerging economies, to further validate the findings and assess the broader implications for healthcare marketing strategies globally.

Abstract Immunotherapy is a promising treatment for cancer that aims to boost the immune system’s response to cancer cells. This can be achieved by blocking Programmed cell death protein 1/Programmed death-ligand 1 (PD1/PDL1), which activates T cells. In this work, the aim was to find high-affinity drugs against PDL1 using computational tools and conjugate nanoparticles with them. The cytotoxic activity of the nanoparticle conjugated drugs was then tested. The screening of 100,000 drugs from the ZINC database and FDA-approved drugs was done computationally. The physicochemical properties and toxicity of the drugs were analyzed using SwissADME and ProTox-II, respectively. Silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs) were synthesized using extracts of Catharanthus roseus flowers and Juglans regia shells, respectively. The characterization of AgNPs and AuNPs was performed using UV–Vis spectroscopy, X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). Their conjugation with the drugs Irinotecan, Imatinib, and Methotrexate was also confirmed using UV–Vis, FTIR, and Dynamic light scattering (DLS). The top screened drugs were ZINC1098661 and 3 FDA-approved drugs (Irinotecan, Imatinib, and Methotrexate). Docking studies revealed that Irinotecan had the highest binding affinity towards PDL1 when conjugated with silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs). The Irinotecan-PDL1 complex was confirmed as the most stable through molecular dynamics simulations. The result of the methylthiazol tetrazolium (MTT) assay showed that conjugated AgNPs and AuNPs with Irinotecan had a higher toxic effect on the A549 cancer cell line than AgNPs and AuNPs conjugated with Imatinib. This study provides a promising avenue for further investigation and development of nanoparticle-drug conjugates as a potential cancer immunotherapy strategy.

Abstract Background Acute ischemic mitral regurgitation (AIMR) is a significant complication of acute coronary syndrome that leads to severe and immediate hemodynamic deterioration and cardiogenic shock. Intra-aortic balloon pumps (IABP) are commonly used to support patients with AIMR as a bridge to surgery, though they may be insufficient in some cases. Case Presentation A 74-year-old male presented with two days of indigestion and evident hypoxia, and an electrocardiogram revealed inferior and lateral ST-elevation myocardial infarction. Angiography demonstrated severe two-vessel coronary disease with a 100% thrombotic occlusion of the second obtuse marginal artery (OM2, culprit lesion) and an 80% stenosis of the proximal left anterior descending artery (LAD). Despite stenting of OM2, the patient remained hypoxic and hypotensive, necessitating escalated support via an IABP. A follow-up echocardiogram revealed severe mitral regurgitation presumed to be AIMR secondary to a ruptured posteromedial papillary muscle with a flail anterior leaflet (A2). Despite aggressive supportive measures with the IABP, the patient’s hemodynamics continued to show cardiogenic shock and clinical status did not improve. However, the patient was required to abstain from surgery for a P2Y12 inhibitor therapy wash out period. Consequently, the IABP was upgraded to Impella 5.5 as bridge-to-surgery support on day 1 post-admission. Subsequently, the patient’s hemodynamics improved, and he underwent a combined mitral valve replacement and coronary artery bypass grafting surgery on day 7 post-admission without incident. The Impella was successfully explanted on day 25 post-admission. Delay in explant was due to hypotension and respiratory status despite normalizing hemodynamics and echocardiogram revealing recovered left ventricular ejection fraction. The patient developed bacterial pneumonia and acute respiratory distress syndrome and expired on day 27 post-admission. Conclusion Although IABP is standard for supporting AIMR patients as a bridge to surgery, it may not provide sufficient hemodynamic support. This case supports a growing body of evidence that alternative forms of hemodynamic support should be considered if the traditional therapeutic modalities for AIMR do not adequately support patients. Clinicians may consider upgrading IABP to Impella to provide increased hemodynamic support and maintain AIMR patient stability while awaiting cardiac surgery.

Introduction: The liver is the body’s largest solid organ and receives a dual blood supply, making it an easy target for metastases from both extra-abdominal and abdominal lesions. In adults, the most common sites of primary lesions are the breast, colon, lung and pancreas. Liver biopsies are pivotal for managing patients with metastatic diseases, aiding in diagnosis and treatment planning. When primary cancer sites are undetectable, the diagnosis often falls under Carcinoma of Unknown Primary (CUP), which carries a poor prognosis. Immunohistochemistry (IHC) is critical in such cases, using antibodies to identify the cancer’s origin when other methods fail. This technique is vital for confirming known malignancies and diagnosing elusive ones, thus informing treatment and improving patient outcomes. Aim: To study the role of IHC in the categorisation of metastatic tumours of the liver. Material and Methods: A cross-sectional observational study was conducted in the Department of Pathology, Sri Aurobindo Medical College and Postgraduate (PG) Institute, Indore, Madhya Pradesh, India, between March 2021 and April 2024. Core needle biopsy samples of 65 cases were processed and analysed for Haematoxylin and Eosin (H&E) stain and IHC markers. Data on demographics and lesion characteristics were entered into Microsoft Excel and analysed with a trial version of Statistical Package for the Social Sciences (SPSS). Significance was assessed using Pearson’s Chi-square test, with a p-value

Abstract The disease dynamics affect the human life. When one person is affected with a disease and if it is not treated well, it can weaken the immune system of the body. Human Immunodeficiency Virus (HIV) is a virus that attacks the immune system, of the body which is the defense line against diseases. If it is not treated well then HIV progresses to its advanced stages and it is known as Acquired Immunodeficiency Syndrome (AIDS). HIV is typically a disease that can transferred from one person to another in several ways such as through blood, breastfeeding, sharing needles or syringes, and many others. So, the need of the hour is to consider such important disease dynamics and that will help mankind to save them from such severe disease. For the said purpose the reaction-diffusion HIV CD4 $$^{+}$$ + T cell model with drug therapy under the stochastic environment is considered. The underlying model is numerically investigated with two time-efficient schemes and the effects of various parameters used in the model are analyzed and explained in a real-life scenario. Additionally, the obtained results will help the decision-makers to avoid such diseases. The random version of the HIV model is numerically investigated under the influence of time noise in It $${\hat{o}}$$ o ^ sense. The proposed stochastic backward Euler (SBE) scheme and proposed stochastic Implicit finite difference (SIFD) scheme are developed for the computational study of the underlying model. The consistency of the schemes is proven in the mean square sense and the given system of equations is compatible with both schemes. The stability analysis proves that both schemes and schemes are unconditionally stable. The given system of equations has two equilibria, one is disease-free equilibrium (DFE) and the other is endemic equilibrium. The simulations are drawn for the different values of the parameters. The proposed SBE scheme showed the convergent behavior towards the equilibria for the given values of the parameters but also showed negative behavior that is not biological. The proposed SIFD scheme showed better results as compared with the stochastic SBE scheme. This scheme has convergent and positive behavior towards the equilibria points for the given values of the parameters. The effect of various parameters is also analyzed. Simulations are drawn to evaluate the efficacy of the schemes.

Abstract An extraordinary resolution down to 50 microns is demonstrated for the first time for bulk ultrasonics, using novel micro-fabricated metamaterial lenses. The development and performance of the silicon-based Fabry–Perot type metalenses with an array of 10 micrometre square holes are discussed. Challenges in wave reception are addressed by a custom-developed micro-focal laser with a sub-micron spot size and an innovative experimental set-up together with physics based signal processing. The results provide a pathway for material diagnostics at greater depths with high resolution using micro-metalens-enhanced ultrasound as an alternative to expensive and radiation prone electromagnetic techniques.

Abstract Background Despite an empathic doctor patient relationship being of utmost importance to improve health care outcomes, this aspect is scarcely explored in dental students of Pakistan. This primary objective of the present study was to assess the reliability and validity of the Jefferson Scale of Empathy- Health Professions Student (JSE- HPS) version in a sample of Pakistani dental students. The study also compared the differences in empathy levels of dental students studying in different academic years. Methods This comparative cross-sectional study was conducted on a sample of 304 students from first to final year from selected 02 private and 02 public dental colleges of Karachi, Pakistan from December 2021- January 2022. The self-administered Jefferson Scale of Empathy- Health Professions Student (JSE-HPS) version was used for data collection. This questionnaire includes 20 items that can be answered on a 7-point Likert scale. After attendance sheets were obtained, random student names were marked, and questionnaire distributed by hand to these students. All forms were collected right after to maximize response rate. Results A total of 304 forms with complete data were returned, a response rate of 86.9%. Females (97.79 ± 15 94) were more empathetic than males (94.16 ± 12.13) (p = 0.001). Students of third-year were the most empathetic (p = 0.000). Internal consistency of questionnaire was acceptable (Cronbach’s α- 0.77). Factor analysis revealed factor related to belief that patient’s perspectives improve health outcome had 14 items with factor coefficient > 0.4 contributing to largest proportion of variance (23.15%). Conclusions Our study shows JSE- HPS to have acceptable internal consistency. Structural validity of the scale evaluated by confirmatory factor analysis reported results that were in concordance to those suggested by developers of this scale. In our study population, like other studies, females were more empathic than males. Third-year dental students were more empathetic than students of other undergraduate years.

Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence interval ranging between 77.43 ± 0.12, (77.08, 77.56) and 94.45 ± 0.12, (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence interval ranging from 72.71 ± 0.19, (72.20, 73.00) to 90.16 ± 0.16, (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer.

Gossypiboma is a unique postoperative complication characterized by the presence of a surgical sponge after surgery. This condition is seldom encountered. Owing to its serious medicolegal implications it remains underreported leading to a blurred clinical spectrum. This fact is also depicted in our case which presents an array of nonspecific symptoms painting a picture of possible malignancy. The case highlights the need for strict adherence to surgical protocols to avoid such instances of clinical negligence. Furthermore, our report provides the medical community with apt data to promptly diagnose such cases. A 40-year-old Asian hypertensive and diabetic female with a history of total hysterectomy presented with a 7-month history of progressive abdominal swelling, vomiting, and weight loss. The initial clinical presentation pointed to possible malignancies but subsequently revealed a 16 × 4 × 6 cm gossypiboma near the ileocecal junction. Following the removal and resection of 2.5 feet of the ileum, the patient underwent a right hemicolectomy, ileostomy, and colostomy. The postoperative recovery was uneventful, and the patient was discharged in stable condition. This case reiterates the importance of adhering to established surgical protocols and how minor negligence can have devastating and horrid effects. This case highlights the need for the fusion of medicine with technology< which prompts the use of innovations such as RFID-tagged sponges and electronic counting to minimize human error. Increased awareness and reporting of gossypibomas will aid in their timely diagnosis and management, ultimately reducing patient morbidity and associated legal challenges.

This study examines the theoretical framework and practical application of machine learning models to predict high run chase outcomes in T-20 International (T-20I) cricket. Focusing on factors such as venue, toss outcomes, batting order, pitch conditions, and team rankings, the analysis investigates how these elements contribute to the probability of achieving high-scoring chases. A dataset comprising 458 T-20I matches from 2005 to 2024 was analyzed using seven machine learning models: Random Forest, Support Vector Machine, Naïve Bayes, Neural Networks, Decision Tree, XGBoost, and K-Nearest Neighbors. Model performance was systematically evaluated through metrics including accuracy, precision, recall, and Area under the Curve (AUC), with robustness confirmed via Monte Carlo simulations across variable match conditions. Results highlight Random Forest and Decision Tree models as the most accurate and reliable, consistent with theoretical expectations for ensemble and decision-based models in complex, multi-factor analyses. This research provides both theoretical and practical insights to enhance data-driven strategies, pre-match evaluations, and in-game decision-making for T-20I cricket performance analysis.Top of Form

This research presents a novel approach to setting up a sustainable Closed-Loop Supply Chain (CLSC) network for used solar photovoltaic (PV) systems, addressing end-of-life product waste from solar panel installations and manufacturing centers. The model accounts for uncertainties in PV systems and aims to efficiently collect, refurbish, and recycle used solar PV systems, promoting a circular and environmentally responsible waste management strategy. The supply chain network comprises vendors, collection centers, hybrid centers, distribution centers, and manufacturing centers, with objectives to maximize total profit, minimize environmental risk, and maximize service levels by demonstrating the profitable reuse of used solar photovoltaic systems by manufacturers. The epsilon-constraint method is utilized to handle the model's multi-objectiveness and identify Pareto optimal solutions. A case study in Iran is conducted to validate the methodology's performance, comparing results obtained from three meta-heuristic methods: Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Gray Wolf Optimization (MOGWO). The average error rates are 0.0358 for MOGWO, 0.1248 for MOPSO, and 0.2066 for NSGA-II. Sensitivity analysis highlights the significant impact of demand variations on all objective functions. Lastly, the numerical results are discussed to provide managerial insights for informed decision-making.