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IntroductionOlder adults with higher needs are ideal candidates for social prescribing interventions, given the complex and intersectoral nature of their needs. This article describes findings from a developmental evaluation of 19 social prescribing programs for older adults at risk of frailty. MethodsAn evaluation of the programs was conducted from 2020 to 2023. We used data from three components of the evaluation: (1) initial evaluation data collected in 2020 and 2021; (2) program profiles developed in 2022; and (3) co-creation sessions conducted in 2023. ResultsFrom startup until March 2023, the programs served a total of 2544 older adults. The community connectors identified factors at the individual, interpersonal, institutional, community and policy levels that contributed to the successful implementation and delivery of their programs (e.g. physician champions, communities of practice, strong pre-existing relationships with the health care system), as well as challenges (e.g. limited capacity of family physicians, lack of community resources). There was strong agreement among community connectors that successful social prescribing programs should include the following core elements: (1) making connections to needed community resources; (2) co-creation of a wellness plan with long-term clients or clients who require intensive supports; (3) ongoing follow-up and check-ins for clients with wellness plans; and (4) an assessment and triaging process for the prioritization of clients. ConclusionTo leverage the full potential of social prescribing interventions, it is essential that programs engage with a range of health and social care providers, that community connectors are skilled and well supported, and that adequate investments are made in the nonprofit and voluntary sector.

IntroductionLes aînés ayant des besoins accrus sont des candidats idéaux pour les interventions de prescription sociale, compte tenu de la nature complexe et intersectorielle de leurs besoins. Cet article présente les résultats d’une évaluation de l’élaboration de 19 programmes de prescription sociale destinés aux aînés à risque de fragilité. MéthodologieNous avons effectué une évaluation des programmes pour la période 2020-2023. Nous avons utilisé les données de trois composantes de l’évaluation : 1) les données de l’évaluation initiale, recueillies en 2020 et en 2021, 2) les profils des programmes élaborés en 2022 et 3) les séances de cocréation tenues en 2023. RésultatsDe leur lancement jusqu’au mois de mars 2023, les programmes ont servi 2 544 aînés. Les agents de liaison communautaire ont cerné les facteurs au niveau individuel, au niveau interpersonnel, au niveau des établissements, au niveau de la collectivité et au niveau des politiques qui ont contribué à la mise en oeuvre et à la prestation de leurs programmes (médecins champions, communautés de pratique, solides relations préexistantes avec le système de santé, etc.), de même que les difficultés qui ont été rencontrées (capacité limitée des médecins de famille, manque de ressources communautaires, etc.). Les agents de liaison communautaire ont convenu qu’un programme de prescription sociale efficace devrait comprendre les éléments essentiels suivants : 1) établir des liens avec les ressources communautaires nécessaires, 2) prévoir la cocréation d’un plan de mieux-être avec les clients ayant besoin d’un soutien de longue durée ou intensif, 3) assurer un suivi et des vérifications de façon continuelle dans le cas des clients ayant des plans de mieux-être et 4) prévoir un processus d’évaluation et de triage pour la priorisation des clients. ConclusionPour tirer pleinement parti des possibilités offertes par les interventions de prescription sociale, il est essentiel que les programmes mobilisent un éventail de fournisseurs de soins de santé et de services sociaux, que les agents de liaison communautaire soient qualifiés et bien appuyés et que des investissements adéquats soient faits dans le secteur des organismes à but non lucratif et du bénévolat.

Abstract Background Artemisinin-based combination therapy (ACT) has been a major contributor to the substantial reductions in global malaria morbidity and mortality over the last decade. In Tanzania, artemether-lumefantrine (AL) was introduced as the first-line treatment for uncomplicated Plasmodium falciparum malaria in 2006. The World Health Organization (WHO) recommends regular assessment and monitoring of the efficacy of the first-line treatment, specifically considering that artemisinin resistance has been confirmed in the Greater Mekong sub-region. This study's main aim was to assess the efficacy and safety of AL for treating uncomplicated P. falciparum malaria in Tanzania. Methods This was a single-arm prospective antimalarial drug efficacy trial conducted in four of the eight National Malaria Control Programme (NMCP) sentinel sites in 2019. The trial was carried out in outpatient health facilities in Karume-Mwanza region, Ipinda-Mbeya region, Simbo-Tabora region, and Nagaga-Mtwara region. Children aged six months to 10 years with microscopy confirmed uncomplicated P. falciparum malaria who met the inclusion criteria were recruited based on the WHO protocol. The children received AL (a 6-dose regimen of AL twice daily for three days). Clinical and parasitological parameters were monitored during follow-up over 28 days to evaluate drug efficacy. Results A total of 628 children were screened for uncomplicated malaria, and 349 (55.6%) were enrolled between May and September 2019. Of the enrolled children, 343 (98.3%) completed the 28-day follow-up or attained the treatment outcomes. There were no early treatment failures; recurrent infections during follow-up were common at two sites (Karume 29.5%; Simbo 18.2%). PCR-corrected adequate clinical and parasitological response (ACPR) by survival analysis to AL on day 28 of follow-up varied from 97.7% at Karume to 100% at Ipinda and Nagaga sites. The commonly reported adverse events were cough, skin pallor, and abdominal pain. The drug was well tolerated, and no serious adverse event was reported. Conclusion This study showed that AL had adequate efficacy and safety for the treatment of uncomplicated falciparum malaria in Tanzania in 2019. The high recurrent infections were mainly due to new infections, highlighting the potential role of introducing alternative artemisinin-based combinations that offer improved post-treatment prophylaxis, such as artesunate-amodiaquine (ASAQ).

Traffic flow prediction is a research topic signified by several researchers in a league span of disciplines. Traffic flow prediction is an important aspect in Intelligent Transport Management System (ITMS). In this context, one of the most in-demand techniques of Machine Learning, especially Time series based techniques, helps in predicting traffic flow forecasting and increases the accuracy of the prediction model. In order to deliver extremely precise traffic forecasts, it is crucial that we put the prediction system into practice in the actual world. Our aim is to perform computations related to traffic on the traffic datasets and find out the accuracy for each model. For this purpose we are using three distinct time series models: Long Short Term Memory (LSTM), the Autoregressive Integrated Moving Average (ARIMA), and the Seasonal Autoregressive Integrated Moving Average (SARIMA). From the results obtained, it is concluded that the proposed model achieves highest prediction accuracy with the lowest root mean squared error.

Yersinia pestis is the perilous pandemics that occurred in Asia and Europe. The bacterium has shown drug resistance that can cause the future pandemic and destroy the drug treatment against plague. As known, effective therapeutics such as designing potent vaccine that can aid world to protect against plague. The immunoinformatics approaches was implemented via different server. The 4 potent antigens (F1 capsule, LcrV, OmpA, and PH6) were listed as essential protein target for creating the multi-epitope vaccine. These targets were selected for designing multi-epitope vaccine that predicted the CTL and HTL epitopes. The vaccine construct included different linkers such as EAAAK, AAY, GPGPG, and SSL that an adjuvant (Beta defensin-3) inserted at N-terminal of vaccine. The computational physiochemical properties and other immunological analysis showed stable, soluble, antigen, non-allergen, and non-toxin. The molecular docking confirmed the stable binding and good interaction and the iMODS server showed the stable binding. Furthermore, computational immune simulation of multi-epitope vaccine showed that vaccine can stimulate adaptive and innate responses after second doses. In this study, the vaccine designed for Y. pestis that in future require immunological examination to unveil real efficiency.

Precise demand forecasting and agile pricing strategies are crucial in modern business. This study aims to enhance these strategies by evaluating the efficacy of Holt-Winters Exponential Smoothing (HWES) and Autoregressive Integrated Moving Average (ARIMA) models. The study assesses their performance in predicting demand amid unpredictable factors and develops robust forecasting algorithms using real-world data. It evaluates HWES and ARIMA in capturing demand fluctuations, considering seasonality, market trends, and cyclic patterns. A comprehensive comparative analysis is conducted under stable and unstable economic conditions. The study also focuses on a dynamic pricing model for limited sale seasons, examining lost sales patterns over time. In the context of supply chain and inventory management, efficient demand forecasting and dynamic pricing are essential for optimizing inventory levels and minimizing costs. Supply chains must adapt quickly to demand fluctuations to avoid overstocking or stockouts, which lead to revenue losses and inefficiencies. The findings reveal that ARIMA consistently outperforms HWES in minimizing lost sales, demonstrating its efficacy in demand forecasting, mitigating stockouts, and reducing revenue losses, particularly in varying economic conditions. This research significantly contributes to current knowledge by developing tailored forecasting algorithms and a dynamic pricing model, enhancing supply chain resilience and performance in uncertain business environments.

ABSTRACT1,2,3-triazole is a five-member nitrogen-containing heterocyclic compound with diverse biological activities. A number of 1,2,3-triazole-containing FDA-approved drugs are available on the market, and many other pharmaceutically significant potential molecules are under clinical investigation. Therefore, it is fairly reasonable to anticipate that there will be an increase in demand for new triazole-containing drugs in the coming future. Because of its broad pharmacological applications, it has recently attracted the attention of scientists worldwide. This review provides a concise summary of the synthetic techniques for triazole compounds from various strategies and their medical importance as a lead structure for the enhancement of therapeutic molecules.

The research presents a novel approach to inventory modelling, emphasizing stochastic demand and dynamic pricing strategies for a seasonal sales framework. The methodology divides the sales season into intervals, each associated with distinct pricing strategies influenced by stochastic factors. The study employ exponential smoothing for demand forecasting, optimizing inventory replenishment and dynamic pricing strategies through developed algorithms. Notably, the study determine the optimal smoothing parameter for demand forecasting, balancing responsiveness to recent demand patterns with long-term stability. Proposed research achieves a comprehensive framework empowering businesses to enhance competitiveness and profitability by addressing challenges of stochastic demand and dynamic pricing. Dynamic pricing strategies outperformed classical strategies, allowing businesses to respond promptly to demand fluctuations and optimize profit margins during sales seasons. Incorporating stochastic demand models enabled organizations to implement safety stock and buffer inventory levels effectively, mitigating risks associated with uncertain demand patterns. Real-time data analysis was crucial for adjusting price dynamics and making effective management decisions, leading to improved financial performance. The iterative nature of dynamic pricing strategies emphasized the importance of continuous refinement to adapt to evolving market dynamics. This approach enables data-driven decisions, adaptation to market fluctuations, and improved inventory management despite unpredictable demand. Ultimately, this study provides valuable insights and methodologies applicable across industries for efficient and profitable inventory management.

Abstract Cyclic high-dose testosterone administration, known as bipolar androgen therapy (BAT), is a treatment strategy for patients with metastatic castration-resistant prostate cancer (mCRPC). Here, we report the results of a multicenter, single arm Phase 2 study (NCT03554317) enrolling 45 patients with heavily pretreated mCRPC who received BAT (testosterone cypionate, 400 mg intramuscularly every 28 days) with the addition of nivolumab (480 mg intravenously every 28 days) following three cycles of BAT monotherapy. The primary endpoint of a confirmed PSA50 response rate was met and estimated at 40% (N = 18/45, 95% CI: 25.7–55.7%, P = 0.02 one-sided against the 25% null hypothesis). Sixteen of the PSA50 responses were achieved before the addition of nivolumab. Secondary endpoints included objective response rate (ORR), median PSA progression-free survival, radiographic progression-free survival (rPFS), overall survival (OS), and safety/tolerability. The ORR was 24% (N = 10/42). Three of the objective responses occurred following the addition of nivolumab. After a median follow-up of 17.9 months, the median rPFS was 5.6 (95% CI: 5.4–6.8) months, and median OS was 24.4 (95% CI: 17.6–31.1) months. BAT/nivolumab was well tolerated, resulting in only five (11%) drug related, grade-3 adverse events. In a predefined exploratory analysis, clinical response rates correlated with increased baseline levels of intratumoral PD-1 + T cells. In paired metastatic tumor biopsies, BAT induced pro-inflammatory gene expression changes that were restricted to patients achieving a clinical response. These data suggest that BAT may augment antitumor immune responses that are further potentiated by immune checkpoint blockade.