Effect of different concentrations of resveratrol on nuclear maturation and in-vitro development competence of oocytes of Nili Ravi buffalo.

Buffaloes are considered animals of the future with the ability to survive under unfavorable conditions. However, the lack of access to superior germplasm poses a significant challenge to increasing buffalo production. Resveratrol has been shown to improve oocyte quality and developmental competence in various animals during in vitro embryo development. However, limited information is available on the use of resveratrol to improve the in vitro maturation and development competence of Nili Ravi buffalo oocytes. Therefore, the current study aimed to investigate the influence of different concentrations of resveratrol on the maturation, fertilization, and development of buffalo oocytes under in vitro conditions. Oocytes were collected from ovaries and subjected to in vitro maturation (IVM) using varying concentrations of resveratrol (0 µM, 0.5 µM, 1 µM, 1.5 µM, and 2 µM), and the maturation process was assessed using a fluorescent staining technique. Results indicated no significant differences in oocyte maturation, morula rate, and blastocyst rate among the various resveratrol concentrations. However, the cleavage rate notably increased with 1 µM and 1.5 µM concentrations of resveratrol (p < 0.05). In conclusion, the study suggests that adding 1 µM of resveratrol into the maturation media may enhance the cleavage and blastocyst hatching of oocytes of Nili Ravi buffaloes. These findings hold promise for advancing buffalo genetics, reproductive performance, and overall productivity, offering potential benefits to the dairy industry, especially in Asian countries.

Market Access Analysis of Biologics and Small-Molecule Inhibitors for Inflammatory Bowel Disease Among US Health Insurance Policies.

BACKGROUND AND AIMS: Treatment pathways for ulcerative colitis (UC) and Crohn's disease (CD) are shifting to a more individualized, risk-stratified approach. The perception is that insurance policies may not have implemented this paradigm shift, particularly regarding access to newer agents. We evaluated patient access to advanced therapies by analyzing policy information from the Managed Markets Insight and Technology database. METHODS: Coverage status as of December 2018 for all US lives was queried for adalimumab, infliximab, infliximab-dyyb, tofacitinib, ustekinumab, and vedolizumab by indication (UC and/or CD) and medical or pharmacy coverage benefit. Coverage status was classified by the number of biologic steps before access to specified drug as "No Biologic," "1 Prior Biologic," "2+ Prior Biologics," "Not Covered." Unknown lives were excluded from the analyses. RESULTS: Coverage analysis was available for approximately 302 million lives under each medical and pharmacy benefit. Our analysis indicates that approximately half of covered lives had access to all agents (except tofacitinib) as first-line therapy; two-thirds had access after one biologic exposure. Among newer agents, vedolizumab had the widest coverage. For indications of UC and CD, 81% of known lives had access to vedolizumab with no prior biologic exposure required ("No Biologic"), 95% after "No Biologic" + "1 prior Biologic." Geographic variations were identified for coverage patterns. CONCLUSIONS: This US-based healthcare policy analysis points to an increased access to advanced therapies for UC and CD. An individualized, risk-stratified treatment approach integrating advanced therapies, including those recently approved, into treatment pathways for UC and CD is feasible.

Initiating immunoglobulin replacement therapy helps reduce severe infections and shifts healthcare resource utilization to outpatient services among US patients with inborn errors of immunity.

AIMS: Patients with inborn errors of immunity (IEI) are predisposed to severe recurrent/chronic infections, and often require hospitalization, resulting in substantial burden to patients/healthcare systems. While immunoglobulin replacement therapies (IgRTs) are the standard first-line treatment for most forms of IEI, limited real-world data exist regarding clinical characteristics and treatment costs for patients with IEI initiating such treatment. This retrospective analysis examined infection and treatment characteristics in US patients with IEI initiating IgRT with immune globulin infusion (human), 10% (IG10%). Healthcare resource utilization (HCRU) and associated costs before and after treatment initiation were compared. Additionally, the impact of COVID-19 on infection diagnoses was evaluated. METHODS: Patients with IEI initiating IG10% between July 2012 and August 2019 were selected from Merative MarketScan Databases using diagnosis/prescription codes. Patients were followed 6 months before and after first IG10% claim date. Demographic and clinical characteristics were described. Treatment characteristics and HCRU before and after IG10% initiation were compared. Infection diagnoses during 2020 and 2019 (March-December) were compared. RESULTS: The study included 1,497 patients with IEI diagnoses (mean age = 43.4 years) initiating IG10%, with frequently reported comorbidities like asthma (32.1%). Following IG10% initiation, fewer severe infection diagnoses (11.6% vs 19.9%), fewer infection-related inpatient (10.8% vs 19.5%) and outpatient services (71.6% vs 79.9%), and lower infection-related total healthcare costs ($7,849 vs $13,995; p < 0.001)-driven by lower inpatient costs ($2,746 vs $9,900)-were observed than before. Fewer patients had infection diagnoses during COVID-19 (22.8%) than the prior year (31.2%). CONCLUSION: Patients with IEI are susceptible to severe infections leading to high disease burden and treatment costs. Following IG10% initiation, we observed fewer infections, lower infection-related treatment costs, and shift in care (inpatient to outpatient) leading to significant cost savings. Among patients with IEI, 27% fewer infection diagnoses were observed during the early COVID-19 lockdown period than the prior year.

Some people are born with inborn errors of immunity, or IEI. This study included 1,497 people with IEI who recently started taking a drug called immunoglobulin therapy. Before taking this drug, the participants got infections easily, were hospitalized often, and had to take other costly medicines. After starting this drug, they had fewer infections and could be treated at the doctor's office. They had fewer infections during the COVID-19 pandemic than before the pandemic.

Evaluation of Factors Contributing to Diagnosis of Crohn's Disease in the Face of Increasing Trend in Pakistan.

Background: Crohn's disease (CD) is characterized by granulomatous inflammation of the digestive tract. Diagnosing CD involves assessing clinical symptoms, radiological and endoscopic findings, and histopathological evidence. Although previously considered a disease in developed countries, CD is increasing in developing nations, but challenges exist in diagnosing CD promptly. This study aims to report diagnostic parameters for early and correct CD diagnosis in Pakistan. Methodology: A retrospective analysis from June 2016 to August 2023 of 22 CD patients was done, by data from medical records, questionnaires completed at diagnosis, and telephonic interviews. Baseline demographic and clinical characteristics were assessed, and patients were categorized using the Montreal classification. Results: CD was diagnosed in 22 patients, with a 1:1 male-to-female ratio with a mean age of 33 years (range 15-55 years). Symptoms at presentation included abdominal pain (95.5%), watery diarrhea (86.4%), fever (31.8%), rectal bleeding (54.5%), and weight loss (81.8%) with 68% having symptoms for over 12 months before diagnosis. Disease characteristics were diverse, with various patterns of involvement and histopathological findings. Conclusions: In resource-limited countries like Pakistan, the timely diagnosis of CD presents a significant healthcare challenge. Therefore, it is necessary to tackle these complex problems by enhancing diagnostic capabilities, raising medical awareness, and improving access to healthcare resources.

Characterizing primary care for patients with major depressive disorder using electronic health records of a US-based healthcare provider.

BACKGROUND: Major depressive disorder (MDD) is predominantly managed in primary care. However, primary care providers (PCPs) may not consistently follow evidence-based treatment algorithms, leading to variable patient management that can impact outcomes. METHODS: We retrospectively analyzed adult patients with MDD seen at Geisinger, an integrated health system. Utilizing electronic health record (EHR) data, we classified patients as having MDD based on International Classification of Disease (ICD)-9/10 codes or a Patient Health Questionnaire (PHQ)-9 score ≥5. Outcomes assessed included time to first visit with a PCP or behavioral health specialist following diagnosis, antidepressant medication switching, persistence, healthcare resource utilization (HRU), and treatment costs. RESULTS: Among the 38,321 patients with MDD managed in primary care in this study, significant delays between diagnosis with antidepressant prescribing and follow-up PCP visits were observed. There was also considerable variation in care following diagnosis. Overall, 34.9% of patients with an ICD-9/10 diagnosis of MDD and 41.3% with a PHQ-9 score ≥15 switched antidepressants. An ICD-9/10 diagnosis, but not moderately severe to severe depression, was associated with higher costs and HRU. More than 75% of patients with MDD discontinued antidepressant medication within 6 months. LIMITATIONS: The study population was comparable with other real-world studies of MDD, but study limitations include its retrospective nature and reliance on the accuracy of EHRs. CONCLUSIONS: Management of patients with MDD in a primary care setting is variable. Addressing these gaps will have important implications for ensuring optimal patient management, which may reduce HRU and treatment medication costs, and improve treatment persistence.

A Hybrid Geometric Spatial Image Representation for scene classification.

The recent development in the technology has increased the complexity of image contents and demand for image classification becomes more imperative. Digital images play a vital role in many applied domains such as remote sensing, scene analysis, medical care, textile industry and crime investigation. Feature extraction and image representation is considered as an important step in scene analysis as it affects the image classification performance. Automatic classification of images is an open research problem for image analysis and pattern recognition applications. The Bag-of-Features (BoF) model is commonly used to solve image classification, object recognition and other computer vision-based problems. In BoF model, the final feature vector representation of an image contains no information about the co-occurrence of features in the 2D image space. This is considered as a limitation, as the spatial arrangement among visual words in image space contains the information that is beneficial for image representation and learning of classification model. To deal with this, researchers have proposed different image representations. Among these, the division of image-space into different geometric sub-regions for the extraction of histogram for BoF model is considered as a notable contribution for the extraction of spatial clues. Keeping this in view, we aim to explore a Hybrid Geometric Spatial Image Representation (HGSIR) that is based on the combination of histograms computed over the rectangular, triangular and circular regions of the image. Five standard image datasets are used to evaluate the performance of the proposed research. The quantitative analysis demonstrates that the proposed research outperforms the state-of-art research in terms of classification accuracy.

Towards social autonomous vehicles: Efficient collision avoidance scheme using Richardson's arms race model.

This paper presents the concept of a social autonomous agent to conceptualize such Autonomous Vehicles (AVs), which interacts with other AVs using social manners similar to human behavior. The presented AVs also have the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. Exploratory Agent Based Modeling (EABM) level of the Cognitive Agent Based Computing (CABC) framework has been utilized to design the proposed social agent. Furthermore, to emulate the functionality of mentalizing and mirroring modules of proposed social agent, a tailored mathematical model of the Richardson's arms race model has also been presented. The performance of the proposed social agent has been validated at two levels-firstly it has been simulated using NetLogo, a standard agent-based modeling tool and also, at a practical level using a prototype AV. The simulation results have confirmed that the proposed social agent-based collision avoidance strategy is 78.52% more efficient than Random walk based collision avoidance strategy in congested flock-like topologies. Whereas practical results have confirmed that the proposed scheme can avoid rear end and lateral collisions with the efficiency of 99.876% as compared with the IEEE 802.11n-based existing state of the art mirroring neuron-based collision avoidance scheme.