Protective potential of Bacillus subtilis (NMCC-path-14) against extraarticular manifestations during acute and sub-acute phase of arthritis using mice model.

Extra-articular manifestations (EAM), which are associated with rheumatoid arthritis (RA), affect the quality of life of patients and are one of the critical causes of early mortality. This study was aimed at investigating whether Bacillus subtilis NMCC-path-14 (1 × 108 CFU/animal/day) could serve as a valuable therapeutic agent in managing EAM using complete Freund's adjuvant (CFA) induced arthritis during acute and sub-acute phases. Arthritis was induced using intra-dermal administration of CFA in the right hind paw of mice on day 1. Dexamethasone (Dexa) (5 mg/kg/day/animal) was used as a standard treatment. Animals in Dexa and Bacillus subtilis concurrent treatment (BS-CT) received treatments on day 1. The Bacillus subtilis pre-treatment (BS-PT) group received a probiotic dose 7 days before arthritis induction. Parameters like body weight, relative organ weight, colon length, hematology, serum biochemistry, antioxidant capacity, and histopathology of liver, kidney, spleen, colon, stress-related behavioral changes, and cortisol levels were evaluated on days 7 (acute) and 14 (sub-acute). Dexa failed to manage the EAM in arthritic mice and instead exacerbated them. On the other hand, B. subtilis NMCC-path-14 significantly declined EAM with no notable side effects, highlighting its safety and effectiveness. The current data show that B. subtilis NMCC-path-14 may be an alternative option for arthritis treatment that can reduce systemic symptoms associated with arthritis. More studies are required to comprehend the underlying mechanisms of mitigating the EAM by B. subtilis NMCC-path-14.

Differential symbiotic compatibilities between rhizobium strains and cultivated and wild soybeans revealed by anatomical and transcriptome analyses.

Various species of rhizobium establish compatible symbiotic relationships with soybean (Glycine max) leading to the formation of nitrogen-fixing nodules in roots. The formation of functional nodules is mediated through complex developmental and transcriptional reprogramming that involves the activity of thousands of plant genes. However, host transcriptome that differentiate between functional or non-functional nodules remain largely unexplored. In this study, we investigated differential compatibilities between rhizobium strains (Bradyrhizobium diazoefficiens USDA110 Bradyrhizobium sp. strain LVM105) and cultivated and wild soybeans. The nodulation assays revealed that both USDA110 and LVM105 strains effectively nodulate G. soja but only USDA110 can form symbiotic relationships with Williams 82. LVM105 formed pseudonodules on Williams 82 that consist of a central nodule-like mass that are devoid of any rhizobia. RNA-seq data revealed that USDA110 and LVM105 induce distinct transcriptome programing in functional mature nodules formed on G. soja roots, where genes involved in nucleosome assembly, DNA replication, regulation of cell cycle, and defense responses play key roles. Transcriptome comparison also suggested that activation of genes associated with cell wall biogenesis and organization and defense responses together with downregulation of genes involved in the biosynthesis of isoprenoids and antioxidant stress are associated with the formation of non-functional nodules on Williams 82 roots. Moreover, our analysis implies that increased activity of genes involved in oxygen binding, amino acid transport, and nitrate transport differentiates between fully-developed nodules in cultivated versus wild soybeans.

Machine learning-based analysis of Ebola virus' impact on gene expression in nonhuman primates.

Introduction: This study introduces the Supervised Magnitude-Altitude Scoring (SMAS) methodology, a novel machine learning-based approach for analyzing gene expression data from non-human primates (NHPs) infected with Ebola virus (EBOV). By focusing on host-pathogen interactions, this research aims to enhance the understanding and identification of critical biomarkers for Ebola infection. Methods: We utilized a comprehensive dataset of NanoString gene expression profiles from Ebola-infected NHPs. The SMAS system combines gene selection based on both statistical significance and expression changes. Employing linear classifiers such as logistic regression, the method facilitates precise differentiation between RT-qPCR positive and negative NHP samples. Results: The application of SMAS led to the identification of IFI6 and IFI27 as key biomarkers, which demonstrated perfect predictive performance with 100% accuracy and optimal Area Under the Curve (AUC) metrics in classifying various stages of Ebola infection. Additionally, genes including MX1, OAS1, and ISG15 were significantly upregulated, underscoring their vital roles in the immune response to EBOV. Discussion: Gene Ontology (GO) analysis further elucidated the involvement of these genes in critical biological processes and immune response pathways, reinforcing their significance in Ebola pathogenesis. Our findings highlight the efficacy of the SMAS methodology in revealing complex genetic interactions and response mechanisms, which are essential for advancing the development of diagnostic tools and therapeutic strategies. Conclusion: This study provides valuable insights into EBOV pathogenesis, demonstrating the potential of SMAS to enhance the precision of diagnostics and interventions for Ebola and other viral infections.

Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images.

Although multiple instance learning (MIL) methods are widely used for automatic tumor detection on whole slide images (WSI), they suffer from the extreme class imbalance WSIs containing small tumors where the tumor may include only a few isolated cells. For early detection, it is important that MIL algorithms can identify small tumors. Existing studies have attempted to address this issue using attention-based architectures and instance selection-based methodologies but have not produced significant improvements. This paper proposes crossattention-based salient instance inference MIL (CASiiMIL), which involves a novel saliency-informed attention mechanism to identify small tumors (e.g., breast cancer lymph node micro-metastasis) on WSIs without needing any annotations. In addition to this new attention mechanism, we introduce a negative representation learning algorithm to facilitate the learning of saliencyinformed attention weights for improved sensitivity on tumor WSIs. The proposed model outperforms the state-ofthe-art MIL methods on two popular tumor metastasis detection datasets. The proposed approach demonstrates great cross-center generalizability, high accuracy in classifying WSIs with small tumor lesions, and excellent interpretability attributed to the saliency-informed attention weights. We expect that the proposed method will pave the way for training algorithms for early tumor detection on large datasets where acquiring fine-grained annotations is is not practical.

Longitudinal detection of prion infection in preclinical sheep blood samples compared using 3 assays.

Variant Creutzfeldt-Jakob disease (vCJD) is a devastating disease caused by transmission of bovine spongiform encephalopathy (BSE) to humans. Although vCJD cases are now rare, evidence from appendix surveys suggests that a small proportion of the UK population may be infected without showing signs of disease. These "silent" carriers could present a risk of iatrogenic vCJD transmission through medical procedures or blood/organ donation, and currently there are no validated tests to identify infected asymptomatic individuals using easily accessible samples. To address this issue, we evaluated the performance of three blood-based assays in a blinded study, using longitudinal sample series from a well-established large animal model of vCJD. The assays rely on amplification of misfolded prion protein (PrPSc; a marker of prion infection), and include real-time quaking-induced conversion (RT-QuIC), and two versions of protein misfolding cyclic amplification (PMCA). Although diagnostic sensitivity was higher for both PMCA assays (100%) than RT-QuIC (61%), all three assays detected prion infection in blood samples collected 26 months before the onset of clinical signs, and gave no false positive results. Parallel estimation of blood prion infectivity titres in a sensitive transgenic mouse line showed positive correlation of infectivity with PrPSc detection by the assays, suggesting that they are suitable for detection of asymptomatic vCJD infection in the human population. This study represents the largest comparison to date of preclinical prion detection in blood samples from a relevant animal model. The outcomes will guide efforts to improve early detection of prion disease and reduce infection risks in humans.

Advancements in application of modified biochar as a green and low-cost adsorbent for wastewater remediation from organic dyes.

Synthetic organic dyes, which are resistant to biodegradation, pose a notable health risk, potentially leading to cancer and respiratory infections. Researchers have addressed this concern by exploring physicochemical methods to remove organic dyes from wastewater. A particularly promising solution involves modified biochar adsorbents, which demonstrate high efficiency in organic dye removal. Biochar, a charcoal-like material derived from biomass pyrolysis, offers advantages such as low cost, eco-friendliness, high efficiency and reusability. Beyond its role in sustainable soil remediation, biochar proves effective in removing organic dyes from wastewater after undergoing physical or chemical modification. Acid-base activation or metal-heteroatom impregnation enhances biochar's adsorption capacity. This comprehensive review examines the attributes of biochar, common methods for production and modification, and the impacts of raw materials, pyrolysis temperature, heating rate and residence time. It further elucidates the biochar adsorption mechanism in the removal of organic dyes, assessing factors influencing efficiency, including biochar feedstock, solution pH, adsorption temperature, particle size, initial dye concentration, biochar dosage and reaction time. It explores challenges, opportunities, reusability and regeneration methods of biochar in treating organic dye wastewater. It also discusses recent advances in organic dye removal using adsorption-based biochar. The review ultimately advocates for enhancing biochar's adsorption performance through post-modification.

Cardiomegaly: Navigating the uncharted territories of heart failure - A multimodal radiological journey through advanced imaging, pathophysiological landscapes, and innovative therapeutic frontiers.

Cardiomegaly is among the disorders categorized by a structural enlargement of the heart by any of the situations including pregnancy, resulting in damage to heart muscles and causing trouble in normal heart functioning. Cardiomegaly can be defined in terms of dilatation with an enlarged heart and decreased left or biventricular contraction. The genetic origin of cardiomegaly is becoming more evident due to extensive genomic research opening up new avenues to ensure the use of precision medicine. Cardiomegaly is usually assessed by using an array of radiological modalities, including computed tomography (CT) scans, chest X-rays, and MRIs. These imaging techniques have provided an important opportunity for the physiology and anatomy of the heart. This review aims to highlight the complexity of cardiomegaly, highlighting the contribution of both ecological and genetic variables to its progression. Moreover, we further highlight the worth of precise clinical diagnosis, which comprises blood biomarkers and electrocardiograms (EKG ECG), demonstrating the significance of distinguishing between numerous basic causes. Finally, the analysis highlights the extensive variation of treatment lines, such as lifestyle modifications, prescription drugs, surgery, and implantable devices, although highlighting the critical need for individualized and personalized care.

Chronic stress intensify PTZ-induced seizures by triggering neuroinflammation and oxidative stress.

BACKGROUND: Epilepsy is a paroxysmal abnormal hypersynchronous electrical discharge characterized by recurrent seizures. It affects more than 50 million people worldwide. Stress is the leading cause of neurodegeneration and can produce seizures that may lead to or aggravate epilepsy. Inflammation plays a vital role in epilepsy by modulating oxidative stress, and levels of neuroinflammatory cytokines including NF-κB, TNF-α, and IL-1ß. METHODS: Stress-induced changes in behavior were evaluated in mice by employing behavioral assessment tests such as an elevated plus maze, light-dark box, open field test, tail suspension test, Y-maze, novel object recognition test, and Morris water maze in pentylenetetrazole (PTZ) kindled mice. Behavioral changes in all these paradigms including seizure score, latency, and frequency showed an increase in symptoms in PTZ (35 mg/kg) induced seizures in stressed mice (RS-PTZ) as compared to PTZ, Stress, and normal animals. RESULTS: The Enzyme-linked immunosorbent assay (ELISA) results confirmed increased in serum cortisol levels. Histological examinations showed neurodegenerative changes in the hippocampus and cortex regions. The spectrophotometric evaluation showed an increase in oxidative stress by decreasing antioxidant production i.e. reduced glutathione, glutathione -s- transferase, and catalase (CAT), and increasing oxidant levels such as maloaldehyde and nitric oxide. Immunohistochemistry results showed increased expression of NF-κB, TNF-α, and IL-1ß in the cortex and hippocampus of mice brains. CONCLUSIONS: Results from the study conclude that stress increases the likelihood of eliciting an epileptic attack by increasing the level of reactive oxygen species and neuroinflammation.

A comparative analysis of RNA-Seq and NanoString technologies in deciphering viral infection response in upper airway lung organoids.

In this study, we delved into the comparative analysis of gene expression data across RNA-Seq and NanoString platforms. While RNA-Seq covered 19,671 genes and NanoString targeted 773 genes associated with immune responses to viruses, our primary focus was on the 754 genes found in both platforms. Our experiment involved 16 different infection conditions, with samples derived from 3D airway organ-tissue equivalents subjected to three virus types, influenza A virus (IAV), human metapneumovirus (MPV), and parainfluenza virus 3 (PIV3). Post-infection measurements, after UV (inactive virus) and Non-UV (active virus) treatments, were recorded at 24-h and 72-h intervals. Including untreated and Mock-infected OTEs as control groups enabled differentiating changes induced by the virus from those arising due to procedural elements. Through a series of methodological approaches (including Spearman correlation, Distance correlation, Bland-Altman analysis, Generalized Linear Models Huber regression, the Magnitude-Altitude Score (MAS) algorithm and Gene Ontology analysis) the study meticulously contrasted RNA-Seq and NanoString datasets. The Magnitude-Altitude Score algorithm, which integrates both the amplitude of gene expression changes (magnitude) and their statistical relevance (altitude), offers a comprehensive tool for prioritizing genes based on their differential expression profiles in specific viral infection conditions. We observed a strong congruence between the platforms, especially in identifying key antiviral defense genes. Both platforms consistently highlighted genes including ISG15, MX1, RSAD2, and members of the OAS family (OAS1, OAS2, OAS3). The IFIT proteins (IFIT1, IFIT2, IFIT3) were emphasized for their crucial role in counteracting viral replication by both platforms. Additionally, CXCL10 and CXCL11 were pinpointed, shedding light on the organ tissue equivalent's innate immune response to viral infections. While both platforms provided invaluable insights into the genetic landscape of organoids under viral infection, the NanoString platform often presented a more detailed picture in situations where RNA-Seq signals were more subtle. The combined data from both platforms emphasize their joint value in advancing our understanding of viral impacts on lung organoids.

B cells in perivascular and peribronchiolar granuloma-associated lymphoid tissue and B-cell signatures identify asymptomatic Mycobacterium tuberculosis lung infection in Diversity Outbred mice.

Because most humans resist Mycobacterium tuberculosis infection, there is a paucity of lung samples to study. To address this gap, we infected Diversity Outbred mice with M. tuberculosis and studied the lungs of mice in different disease states. After a low-dose aerosol infection, progressors succumbed to acute, inflammatory lung disease within 60 days, while controllers maintained asymptomatic infection for at least 60 days, and then developed chronic pulmonary tuberculosis (TB) lasting months to more than 1 year. Here, we identified features of asymptomatic M. tuberculosis infection by applying computational and statistical approaches to multimodal data sets. Cytokines and anti-M. tuberculosis cell wall antibodies discriminated progressors vs controllers with chronic pulmonary TB but could not classify mice with asymptomatic infection. However, a novel deep-learning neural network trained on lung granuloma images was able to accurately classify asymptomatically infected lungs vs acute pulmonary TB in progressors vs chronic pulmonary TB in controllers, and discrimination was based on perivascular and peribronchiolar lymphocytes. Because the discriminatory lesion was rich in lymphocytes and CD4 T cell-mediated immunity is required for resistance, we expected CD4 T-cell genes would be elevated in asymptomatic infection. However, the significantly different, highly expressed genes were from B-cell pathways (e.g., Bank1, Cd19, Cd79, Fcmr, Ms4a1, Pax5, and H2-Ob), and CD20+ B cells were enriched in the perivascular and peribronchiolar regions of mice with asymptomatic M. tuberculosis infection. Together, these results indicate that genetically controlled B-cell responses are important for establishing asymptomatic M. tuberculosis lung infection.

In vivo evaluation of efficacy and safety of Coagulansin-A in treating arthritis.

The current study aimed to determine the safety and efficacy of Coag-A through in vivo analysis in CFA induced mice model. Treatment of CFA induced arthritis in mice with Coagulansin-A (10 mg/kg i.p. daily for 28 days), a withanolide obtained from Withania coagulans, as well as standard drug treatment with Dexamethasone (5 mg/kg i.p) was provided. The effect of Coag-A on body weight, relative organ weight, hematology, serum biochemistry, survival rate, oxidative stress markers, and antioxidant enzymes was evaluated. The liver and kidney histopathology were also assessed to ascertain its safety profile. Treatment of arthritic mice with Coag-A considerably improved body weight, relative organ weight of liver, kidney, and spleen, ameliorated hematology and serum biochemistry, and increased survival and antioxidant potential. Coag-A was found to be safer with fewer adverse effects showing hepato-protective, nephroprotective, and anti-inflammatory effect. It also significantly (p < 0.001) improved histopathology of CFA-induced mice when compared with Dexa. In conclusion, compared to dexamethasone, Coag-A has demonstrated a greater therapeutic benefit and fewer side effects in the treatment of arthritis against the CFA-induced model.

NiO thickness measurement using a rectangular-type Sagnac interferometer as the material transport layer in a perovskite solar cell.

This work aims to utilize a phase-shifting technique in a rectangular-type Sagnac interferometer (RTSI) to measure the thickness of a thin film of nickel (II) oxide (NiO) in an electron transport layer (ETL) in perovskite solar cell preparation. The NiO layer is deposited on a fluorine-doped tin oxide (FTO) glass substrate. In the RTSI setup, the signal output from the interferometer is divided into the reference and testing arms using a nonpolarizing beam splitter (NPBS). The balanced photodetectors then detect the signal, with the FTO/NiO layer placed in the testing arm and pure FTO in the reference arm. By analyzing the signal intensities at polarization settings of 0° to 180°, the phase shift and thickness of the NiO layer can be determined. The thickness values of FTO and NiO films obtained through three different phase-shifting algorithms of three-, four-, and five-steps are calculated. The obtained NiO thickness values are validated against scanning electron microscopy (SEM). Finally, by considering the NiO thickness value that exhibits the lowest percentage error compared to one from SEM, it is confirmed that the three-step algorithm is the most suitable scheme for obtaining intensities at 0°, 45°, and 90°. Therefore, the proposed setup shows promise as a replacement for SEM in thickness measurements.

Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging.

BACKGROUND AND OBJECTIVES: Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on prechemotherapy cross-sectional imaging to predict patients' response to neoadjuvant chemotherapy. METHODS: Adult patients with colorectal liver metastasis who underwent surgery after neoadjuvant chemotherapy were included. A DLM was trained on computed tomography images using attention-based multiple-instance learning. A logistic regression model incorporating clinical parameters of the Fong clinical risk score was used for comparison. Both model performances were benchmarked against the Response Evaluation Criteria in Solid Tumors criteria. A receiver operating curve was created and resulting area under the curve (AUC) was determined. RESULTS: Ninety-five patients were included, with 33,619 images available for study inclusion. Ninety-five percent of patients underwent 5-fluorouracil-based chemotherapy with oxaliplatin and/or irinotecan. Sixty percent of the patients were categorized as chemotherapy responders (30% reduction in tumor diameter). The DLM had an AUC of 0.77. The AUC for the clinical model was 0.41. CONCLUSIONS: Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.

Aspartate Aminotransferase-to-Platelet Ratio Index (APRI) as a Novel Score in Early Detection of Complicated Dengue Fever.

Introduction: The occurrence of dengue fever presents a considerable burden for public health care in developing countries. This study aims to validate APRI as predictor score for severity of dengue fever so that catastrophic events could be prevented, and early triage can save lives. Methods: The retrospective cross-sectional study was done on dengue positive patients from August to November 2023. APRI score was calculated for every patient at the time of admission. The primary end-point was non-complicated disease (Simple dengue fever) vs complicated disease (dengue hemorrhagic fever and dengue shock syndrome). ROC curve was used to identify the role of APRI in prediction of dengue complication. Youden index was used to find the cut-off value of APRI along with sensitivity, specificity, positive and negative likelihood ratios. To further evaluate the role of APRI score, patients were divided into two groups, patients with APRI score greater and lesser than cut-off value. The qualitative variables among two groups were compared by chi-square testing. The predictors of complicated dengue were first determined by univariate regression analysis and then confirmed by multivariate regression analysis. Results: The mean APRI score of 135 patients was 20.06 ± 6.31. AUC for APRI score was 0.93 (p < 0.0001) indicating that APRI score calculated at the time of admission is an excellent marker in determining the complicated dengue. The cut-off value for APRI score was 9.04 (sensitivity 84.91%, specificity 89.02%, p < 0.0001). The patients with APRI <9.04 mostly developed simple dengue fever (54.1%) vs DHF (4.4%) and DSS (1.5%), while patients with APRI >9.04 had more DHF (20.7%) and DSS (12.6%) vs simple dengue fever (6.7%). None of the patient died with APRI <9.04 while the mortality rate was 3.7% in patients with APRI >9.04. Conclusion: The APRI score, calculated at the time of admission, is an excellent marker in determining the severe dengue.

Risk of infection with arboviruses in a healthy population in Pakistan based on seroprevalence.

Infectious diseases caused by arboviruses are a public health concern in Pakistan. However, studies on data prevalence and threats posed by arboviruses are limited. This study investigated the seroprevalence of arboviruses in a healthy population in Pakistan, including severe fever with thrombocytopenia syndrome virus (SFTSV), Crimean-Congo hemorrhagic fever virus (CCHFV), Tamdy virus (TAMV), and Karshi virus (KSIV) based on a newly established luciferase immunoprecipitation system (LIPS) assays, and Zika virus (ZIKV) by enzyme-linked immunosorbent assays (ELISA). Neutralizing activities against these arboviruses were further examined from the antibody positive samples. The results showed that the seroprevalence of SFTSV, CCHFV, TAMV, KSIV, and ZIKV was 17.37%, 7.58%, 4.41%, 1.10%, and 6.48%, respectively, and neutralizing to SFTSV (1.79%), CCHFV (2.62%), and ZIKV (0.69%) were identified, as well as to the SFTSV-related Guertu virus (GTV, 0.83%). Risk factors associated with the incidence of exposure and levels of antibody response were analyzed. Moreover, co-exposure to different arboviruses was demonstrated, as thirty-seven individuals were having antibodies against multiple viruses and thirteen showed neutralizing activity. Males, individuals aged ≤40 years, and outdoor workers had a high risk of exposure to arboviruses. All these results reveal the substantial risks of infection with arboviruses in Pakistan, and indicate the threat from co-exposure to multiple arboviruses. The findings raise the need for further epidemiologic investigation in expanded regions and populations and the necessity to improve health surveillance in Pakistan.

Shaping the future of preclinical development of successful disease-modifying drugs against Alzheimer's disease: a systematic review of tau propagation models.

The transcellular propagation of the aberrantly modified protein tau along the functional brain network is a key hallmark of Alzheimer's disease and related tauopathies. Inoculation-based tau propagation models can recapitulate the stereotypical spread of tau and reproduce various types of tau inclusions linked to specific tauopathy, albeit with varying degrees of fidelity. With this systematic review, we underscore the significance of judicious selection and meticulous functional, biochemical, and biophysical characterization of various tau inocula. Furthermore, we highlight the necessity of choosing suitable animal models and inoculation sites, along with the critical need for validation of fibrillary pathology using confirmatory staining, to accurately recapitulate disease-specific inclusions. As a practical guide, we put forth a framework for establishing a benchmark of inoculation-based tau propagation models that holds promise for use in preclinical testing of disease-modifying drugs.

Comparing the differences in quality profiles and antioxidant activity in seven pumpkin cultivars (Cucurbita moschata and Cucurbita maxima) at harvest and during postharvest storage.

Pumpkin, nutritious vegetable, is renowned for its extended shelf life. In this study, seven pumpkin cultivars from Cucurbita moschata and Cucurbita maxima were comparatively characterized for 25 physiochemical quality factors, starch granule structures, antioxidant activity, and correlations at 0-60 days of postharvest (dop). The findings revealed that sucrose and carotenoid contents increased in C. moschata, while they initially increased and then decreased in C. maxima. Additionally, acidity, primarily driven by malic acid, decreased in C. maxima but increased in C. maxima. The starch content of C. moschata and C. maxima reached its maximum value at 30 dop and 20 dop, respectively. The DPPH radical scavenging activity correlated with the carotenoid content in both pumpkin species. Conclusively, C. moschata demonstrated improved nutritional and quality at 20-30 dop, while C. maxima exhibited higher commercial suitability at 10-20 dop. The findings suggested that pumpkin storage was crucial for quality improvement.

The effectiveness of emerging markets' legal structure in explaining financial development.

This article examines the role of legal structure in explaining financial development in twenty-three emerging markets, which has not been explored in institutional economics literature before. This study relied on Pedroni, and Kao cointegration tests, which is followed by the renowned panel cointegration technique. The results of the Pedroni and Kao cointegration tests show that the variables understudy is cointegrated in the long-run. These findings are confirmed by the panel cointegration showing that legal structure (LS) has positive impact on financial development (FIND) in long-run that support Law and Finance, and New Institutional Economics theories in emerging markets. This study is the first to directly examine the long-run impact of LS on FIND in emerging markets, and the result remains consistent across alternative measure of FIND. The findings of this study have important policy implications for emerging markets. Policymakers should focus on creating a legal environment that is conducive to financial development. This includes strengthening the legal framework, improving regulatory regimes, and promoting market autonomy. Additionally, policymakers should work to attract foreign investment, which can help spur economic growth and development in emerging markets. The findings of the study are consistent across battery of robustness testing.

Quantification of interstitial cells of Cajal and fibrosis during gastric per-oral endoscopic myotomy and its association with clinical outcomes.

Background and study aims Alterations to interstitial cells of Cajal (ICC) and collagen fibrosis have been implicated in the pathogenesis of gastroparesis. We aimed to evaluate the feasibility and safety of pyloric muscle sampling during gastric peroral endoscopic myotomy (G-POEM) and the association between pyloric ICC density and degree of fibrosis with clinical outcomes. Patients and methods This was a single-center prospective study of gastroparetic patients who underwent G-POEM and intraprocedural pyloric muscle biopsies between January 2022 and April 2023. ICC count was estimated using CD117 stain and trichome for collagen fibrosis. Clinical response to G-POEM was defined as an improvement of ≥ 1 point on the Gastroparesis Cardinal Symptom Index. Results Fifty-six patients (median age 60 years, 71.4% women) underwent G-POEM (100% technical success; 71.4% clinical response). ICC depletion (< 10/high-power field) and fibrosis were encountered in 70.4% and 75% of the cases, respectively. There was no difference in mean ICC count between G-POEM responders vs. non-responders (7±3.6 vs. 7.7±3.3; P = 0.9). There was no association between ICC density or degree of fibrosis with the etiology of gastroparesis, duration of symptoms, gastric emptying rate, or pyloric impedance planimetry. Patients who did not respond to G-POEM had a significantly higher degree of moderate/severe fibrosis when compared with those who responded (81.3% vs. 25%; P = 0.0002). Conclusions Pyloric muscle biopsies during G-POEM was feasible and safe. ICC depletion and pyloric muscle fibrosis are common in gastroparetic patients. The degree of fibrosis may be related to pyloric dysfunction and clinical response to G-POEM. Additional studies are needed to confirm these results.

From darkness to light: Unveiling the asymmetric nexus between energy poverty and environmental quality in South Asia.

Energy poverty alleviation has emerged as a critical economic problem in recent years. Given the enormous number of people without essential energy services, a crucial concern is whether providing universal access to electrification will considerably affect environmental quality. The present research evaluates the asymmetric energy poverty-environmental quality nexus in South Asian economies. Previous works adopted panel data techniques, resulting in distinctive conclusions about the energy poverty-environmental quality nexus, irrespective of the truth that several nations could not establish such a correlation separately. This research, conversely, applies the Quantile-on-Quantile methodology, which enables independent determinations of time-series interconnection in all nations to offer worldwide yet economy-particular evidence concerning the relationship between the variables. The results indicate that energy poverty degrades environmental quality in most selected economies at particular data distribution quantiles. Moreover, the findings disclose that the ranks of asymmetries between the variables change by country, emphasizing the requirement of governments to take special care when accepting policies linked to energy poverty and environmental quality.