Abstract: Psychosocial health challenges remain pervasive among Southern African Indigenous communities, yet prevailing Western-oriented intervention models often inadequately address the cultural and contextual realities of these populations. This disconnect underscores the need for culturally congruent frameworks grounded in Indigenous knowledge systems and community participation. This study aims to confirm and validate a hypothetical model for psychosocial health management among Southern African Indigenous communities, developed during an earlier phase of a broader community-informed research project. A non-experimental quantitative design was employed to assess the model’s content validity and comprehensiveness. Expert evaluation was conducted using the Content Validity Index (CVI) and Content Validity Ratio (CVR), and inter-rater reliability was assessed using Kappa statistics. The findings demonstrate that psychosocial health is best understood as a lifelong process of achieving and maintaining balance across interconnected social, cultural, spiritual, psychological, and communal domains. The model was confirmed as both content valid and comprehensive, highlighting the significance of community participation, shared belief systems, spiritual realm, resilience, and culturally embedded healing practices in the management of psychosocial health. Its confirmation provides a critical foundation for future intervention development, policy development, and psychosocial health practice within Indigenous contexts.

Abstract: Amusia, or tone deafness, is an auditory disorder affecting music processing. This study aimed to compare performance on a newly developed hearing screening test for amusia between typical students and students with learning difficulties. It also examined whether variables such as gender, age, and music education influence amusia detection. The screening tool, developed through laboratory evaluation, comprises seven acoustic subtests ("dissonant intervals", "out of tone", "contour", "memory", "rhythm", "integration", and "emotion") using piano timbre in a major mode. Participants included 100 typical students and 100 students with learning difficulties (aged 6–12 years) with normal hearing. The 15-minute test was administered via headphones. The results showed that while only 8% of typical students scored in the amusia range, 53% of students with learning difficulties failed the test, highlighting a high comorbidity between the two conditions. Furthermore, variables such as gender and music education did not significantly affect test performance, whereas age appeared to influence the results, although this finding is not widely validated by other research. Validation of the tool against the Montreal Battery of Evaluation of Amusia (MBEA) demonstrated high sensitivity and specificity. These findings support the link between amusia and learning difficulties, showcasing the screening tool's practical and scientific value for both pediatric and general populations.

Abstract: Quantifying the spatial complexity of three-dimensional point clouds remains challenging, particularly for hierarchical structures such as protein aggregates. Here, we propose a two-stage method that combines spatial clustering with the Grassberger–Procaccia correlation dimension D₂ to decompose global dimensionality into intra- and inter-cluster contributions. We introduce the parameter Δ=D_2,glob-⟨D_2,clust⟩, which quantifies the excess dimension arising from the mutual arrangement of clusters—gaps, loops, and connecting elements—relative to their internal structure. We validate the method on protein systems undergoing well‑characterized α→β transitions, including prion protein (Protein Data Bank (PDB) [1] entries 1QLX, 6UUR), Aβ(1--40), α-synuclein, IAPP, and the chemokine XCL1. For all fibrillar and oligomeric states analyzed, Δ is positive and statistically significant (threshold Δ>0.07 at 95% confidence), whereas monomeric states yield Δ≈0 . Furthermore, Δ discriminates between Aβ fibril morphotypes (2lmn: Δ = 0.07-0.12; 2lmp: Δ = 0.21-0.31). These results demonstrate that Δ is sensitive to hierarchical architecture, providing a robust and interpretable metric for distinguishing structural states in complex three-dimensional point clouds.

Abstract: Large language models (LLMs) can generate fluent clinical text but remain limited by non deterministic numerical reasoning, lack of direct access to updated guidelines, and limited observability of the process leading to a recommendation. We present CardioAgent, a proof-of- concept cardiovascular clinical decision-support architecture that embeds an LLM within a stateful Reasoning–Acting–Observing workflow. The system combines a LangGraph-based ReAct agent, a suite of deterministic clinical tools, retrieval-augmented generation (RAG) over European Society of Cardiology (ESC) guidelines, session memory, structured output, an operational audit trail, and a numerical consistency checker. The same base model was evaluated in three progressively more structured configurations: a generic zero-shot LLM, a cardiology-specialized LLM, and the complete agentic architecture. In a controlled set of nine synthetic cardiovascular cases, weighted accuracy increased from 55.6% for the generic LLM to 66.7% after domain prompting and to 100% with the complete agent. The complete system achieved a mean normalized keyword recall of 93.5%, invoked the expected tool in 88.9% of cases, and used 2.2 tools per query on average. Mean latency was 17.7 s and median latency was 8.9 s; one case with repeated retrieval calls required 82.3 s. The observed 9/9 accuracy corresponded to a 95% Wilson confidence interval of 70.1–100%. On a separate, author-constructed set of ten cardiology multiple-choice questions inspired by MedQA/USMLE, the tool-guided pipeline selected all expected answers correctly (95% Wilson confidence interval, 72.2–100%). Functional safety checks correctly handled four anomalous inputs, verified nine of nine correct numerical claims, and detected five of five intentionally altered values. These results demonstrate technical feasibility and suggest that deterministic tools, guideline retrieval, and action-level traceability can improve the controllability of LLM-based clinical support. However, the small synthetic test sets, keyword-based scoring, absence of independent cardiologist adjudication, and lack of real-world validation preclude conclusions about clinical effectiveness or safety.

Abstract: Caffeine (1,3,7-trimethylxanthine) is a widely consumed psychoactive drug and neurostimulant, yet its molecular organization and permeation behavior in lipid membranes not fully understood. In the present study, we employ microseconds-long, united-atom Molecular Dynamics simulations to investigate caffeine interactions with a solvated DPPC (1,2-dipalmitoyl-sn-glycero-3-phosphocholine) bilayer at its fluid phase. Caffeine molecules initially aggregate in the aqueous phase to form ordered stackings, which successively permeate into the membrane. The stacked assemblies gradually dissolve, reaching a stable dispersed state where caffeine molecules preferentially reside near the headgroup–acyl chain interface and orient parallel to the lipid acyl chains, consistent with previous experimental and simulation studies. Simulations initiated with caffeine in the membrane core converge to the same equilibrium state, indicating a preferred localization at the interface region. Density profiles and caffeine distributions suggest that direct translocation across the membrane is unlikely. Caffeine partitioning transiently reduces slightly bilayer thickness and increases the membrane surface area, while enhancing acyl chain stiffness near the hydrophilic part of the membrane. These observed trends are reproducible over different system sizes and independent simulations. Overall, this study provides atomic-level insights into the caffeine permeation process, including its effect on the lipid bilayer structure.

Abstract: Environmental stimulation on stroke units (SUs) may affect hemodynamic stability, but the impact of direct and indirect visiting exposure in shared rooms has not been well characterized. This study aimed to assess whether real-time exposure to visits directed to the study patient or to a roommate is associated with blood pressure (BP) variability. We performed a single-center retrospective cohort study. Automated blood pressure measurements were linked to nurse-recorded visitor status in consecutive acute ischemic stroke patients admitted to a university stroke unit over 3 months. Exposure was classified as no visitors, roommate visitors, or own visitors. Mixed-effects models adjusted for time of day and clinical covariates were used. A total of 8,963 blood pressure readings from 87 patients were analyzed: no visitors accounted for 6,607 (73.7%) readings, roommate visitors for 823 (9.2%), and own visitors for 1,533 (17.1%). Visiting exposure showed time-dependent hemodynamic effects. Roommate visits were associated with higher pulse pressure (PP), systolic blood pressure (SBP), and mean blood pressure (MBP) at selected morning, early afternoon, and evening time points, whereas own visits were associated with lower PP at multiple time points. At 09:00, maximal mean BP differences between roommate visitors and own visitors reached 21 mmHg for PP and 27 mmHg for SBP. Visitor presence was associated with short-term, time-dependent BP variability in the early post-stroke period. In shared SU rooms, roommate visitors may represent a modifiable environmental stressor. Ultimately, biobehaviorally informed visiting practices, strategic room allocation, and stimulus-reduction strategies may help optimize hemodynamic stability during acute stroke recovery.

Abstract: The integration of photovoltaic (PV) generation into power systems requires design methods that evaluate not only annual energy yield or installed capacity, but also the dependable contribution of the plant under critical supply conditions. This paper presents a prototype-driven methodology for the physical validation and optimal sizing of a grid-connected PV generation system aimed at estimating, auditing, and evaluating ENFICC-oriented photovoltaic sizing alternatives under the Colombian reliability framework. The workflow integrates three input databases: a ten-year hourly meteorological database from NASA POWER for 2016--2025, a filtered and expanded PV module catalog based on the CEC database, and a filtered and expanded inverter catalog based on the Sandia database. These inputs feed a sequential modeling chain that estimates clearness index, irradiance decomposition, plane-of-array irradiance, cell temperature, DC power, AC power, monthly equivalent daily energy, net effective capacity, regulatory upper bound, and final ENFICC. The methodology was first validated using the installed PV system in La Tebaida, Colombia, as a local physical reference. For the validation case, the model used 252 Jinko Tiger Neo JKM625N-78HL4-BDV modules, two Solis 60K-LV-5G inverters, 157.5 kWp DC, 120 kW AC, fixed topology, and an hourly local validation dataset derived from the installed system. The NASA-based physical validation produced a critical-month energy of 548.5~kWh/day and a final ENFICC of 438.8~kWh/day after applying the secondary-data factor $f_{\mathrm{sec}}=0.8$. After applying the corrected physical filters and monthly coverage checks to the NASA POWER physical irradiance series, ten independent GA runs produced final ENFICC values between 283.5 and 384.2 kWh/day, with a mean of 349.7 kWh/day. The fitness function in all GA runs was evaluated using the corrected NASA POWER physical irradiance series, not an alternative irradiance series. These corrected results do not support the previously inflated optimization values; instead, they show that the local physical validation stage is essential before interpreting GA outputs. The proposed methodology contributes to clean-energy planning by linking renewable-energy integration, physical validation, reproducible computation, and regulatory firm-energy estimation within a single engineering workflow.

Abstract: In order to clarify effects of the volcanic activity on hydrological, thermal and chemical features of a crater lake, Lake Katanuma, Japan, temperature and electric conductivity at 25 °C (EC25) of lake water were monitored at two maximum-depth points in 2023-2025. Under closed condition of the lake without river outflow, the lake is characterized by the high acidity of pH = 2 on average and the existence of the surrounding geothermal area. A seasonal variation of the thermal and chemical features for the lake was shown by sporadic vertical profiling (0.1 m pitch) of temperature, EC25 and dissolved oxygen (DO) at the two points. A groundwater flow system in the lake was specified by quantifying the groundwater inflow G_in and groundwater outflow G_out in the lake from the estimate of hydrological and chemical budgets under non precipitation. During the pycnal stratification of May – September, the lake exhibited the four-layer structure with different temperature and EC25 values, probably due to the lower intrusion of groundwater passing through three different pathways. The four-layer structure disappeared by the vertical mixing in October – December and March – April and the inverse stratification in January – February. The G_out and G_in values were estimated for three budget periods in the mixing season. Applying the Darcy’s law to the G_out values as a confined groundwater flow, it is found out that G_out has a linear relationship with lake volume. The relatively high G_in and G_out estimated is probably caused by the high permeability of lacustrine sand and gravel deposited as bedrock, which furnishes the high adjustability to lake level, i.e., its small variation at ca. 0.5 m in amplitude.

Abstract: Antimicrobial resistance (AMR) is a growing worldwide challenge, treatment options for bacterial infections are increasingly limited. Non-typhoidal Salmonella (NTS), particularly Salmonella enterica serovar Typhimurium, poses a serious threat to public health due to rising antimicrobial resistance. It can cause invasive bloodstream infections that have a high death rate. This study aimed to identify and isolate exclusively lytic bacteriophages that target multidrug-resistant Salmonella Typhimurium (ATCC 13311) from river water and poultry farm environments in Addis Ababa, Ethiopia and assessed their potential as an alternative treatment. A total of 38 environmental samples were collected from poultry farms and rivers. From these samples, 22 different bacteriophages were successfully isolated using S. Typhimurium ATCC 13311 as the host strain. The majority (63.6%) were isolated from river water samples. Spot assays were used to isolate phages, and the double-layer agar method (DLA) was used for purification. Characterization of phages of phages performed by PCR-based identification and classification, latent period determination, plating efficiency, burst size, stability testing, and assessment of in vitro bactericidal activity. Host range analysis showed that all isolates were active against the primary host, and four isolates showed cross-genus efficacy against specific pathotypes of Escherichia coli. The latent periods of the isolated phages ranged from 10 to 20 minutes, indicating a wide range of infection kinetics. Some of the most productive isolates attained Peak titers as high as 10¹³ PFU/mL, indicating significant replication capacity. Additionally, the ideal multiplicity of infection differed significantly amongst isolates, ranging from 0.001 to 100. Regarding environmental stability, all phages were active in a pH range of 5 to 9 and withstood temperatures ranging from 4°C to 37°C. Molecular analysis classified most isolates into the T4-like and T7-like lineages, two distinct phage groups with known lytic characteristics. This study demonstrates that locally isolated lytic bacteriophages can effectively inhibit multidrug-resistant Salmonella Typhimurium ATCC 13311. The findings support their potential as safe and effective biocontrol agents for combating MDR S. Typhimurium infection. However, clinical trials, in vivo research, and genome sequencing are necessary to confirm these phages' effectiveness and safety.

Abstract: Industry 4.0 quality control demands high-precision instance segmentation, yet such resource-intensive vision models require cost-prohibitive infrastructure, restricting adoption among Small and Medium Enterprises (SMEs). This study addresses this trade-off via a synchronized, three-layered Cyber-Physical System (CPS) optimized for low-cost edge nodes. In this cloud-to-edge framework, an ONNX-optimized YOLOv8s-seg model, trained on an augmented dataset of 1,331 industrial proxy images, is deployed onto an accessible $300 CPU-based edge computing node. To avert computational backlogs, the system decouples high-speed digital inference from an asynchronous n8n-powered IoT pipeline and an ESP32-actuated, 3D-printed mechanical sorting testbed. Results show the digital inference layer achieves peak bounding-box and mask accuracies of 93.8% and 93.7% mAP@0.5, with an average end-to-end latency of 605 ms. The physical actuation mechanism maintains a measured throughput of approximately 593 units per hour, consistent with the servo’s ~5,000 ms nominal recovery window, while the IoT layer records a 3.5 s cloud reporting latency. Built at a total hardware cost of approximately $1,543, this system offers a scalable blueprint for edge-AI deployment, showing that careful software-hardware orchestration can mitigate resource constraints and provide an accessible pathway for machine vision and digital traceability in resource-limited manufacturing under the tested proxy-based conditions.

Abstract: The office building sector concentrates a significant proportion of urban energy consumption and carbon emissions, while simultaneously facing the organizational reconfiguration driven by the COVID-19 pandemic and the growing demand for occupant well-being. The objective of this study was to comprehensively analyze design principles, environmental, economic and social impacts, certification frameworks and barriers to implementing sustainable architecture in office spaces. A qualitative approach of documentary review was adopted, inspired by the PRISMA-ScR protocol, through categorical thematic analysis of 63 sources indexed in Scopus and ScienceDirect, selected from an initial corpus of 152 records after the application of inclusion and exclusion criteria. The findings show that the integration of passive strategies, indoor vegetation and smart technologies (BIM, IoT, artificial intelligence) reduces operational energy consumption between 15% and 70%, while the life cycle assessment allows for a reduction of up to 50% in environmental impacts through recycled materials and envelope insulation. Certified buildings report productivity increases of between 2.5% and 26%, cognitive performance improvements of up to 61% and rent and market value premiums of between 10% and 12% in Europe and the United States, although in emerging economies adoption faces cost overruns of between 5% and 21% with payback periods of less than four years. Relevant gaps persist in the integration of social criteria within certification systems (DGNB, LEED, BREEAM) and financial, organizational and technical training barriers that limit the adoption of BIM and sustainable materials, particularly in Latin American contexts. It is concluded that the sustainability of contemporary offices requires a holistic framework that articulates technology, public policy and organizational culture, with direct implications for curricular updating in architecture and construction management programs.

Abstract: Traditional multi-objective evolutionary algorithms for small molecules suffer from structural homogenization and premature convergence, as they only optimize property space without controlling molecular topology diversity. This work proposes MOD-PCAC, a principal component analysis (PAC) and K-Means clustering based optimization framework. Normalized property vectors and dimension-reduced molecular fingerprints are fused to build a joint feature space that embeds structural information into evolutionary screening. A clustering-guided update strategy is designed: inter-cluster sparsity allocates retention quotas, while dynamic Tanimoto thresholds filter redundant structures; cross-cluster crossover broadens chemical space exploration. Six benchmark datasets and four quantitative metrics are adopted for evaluation. Comparative and ablation experiments verify that MOD-PCAC achieves superior convergence and richer scaffold diversity, effectively improving sampling efficiency for multi-property molecular computational optimization.

Abstract: The spatial distribution of Ground Control Points (GCPs) is a critical factor affecting the accuracy of UAV photogrammetry in hilly terrain. Existing studies primarily focus on the influence of GCP quantity on accuracy or discuss planar distribution uniformity in flat areas, with limited systematic analysis separating horizontal and vertical distributions as independent dimensions. This study utilizes a DJI Mavic 3E UAV to acquire aerial imagery in a typical hilly area of Mengyin County, Shandong Province, with 27 high-precision GCPs deployed. Four comparative experiments combining random/uniform distributions in both horizontal and vertical dimensions are designed to quantitatively analyze the impact of different distribution patterns on aerial triangulation and mapping accuracy. Results demonstrate that the dual-uniform distribution strategy achieves optimal accuracy, with horizontal RMSE of 0.045 m and vertical RMSE of 0.039 m, representing improvements of 32.8% and 17.0% respectively compared to the random distribution scheme. Furthermore, this paper proposes the Spatial Distribution Balance Index (SDBI), which integrates Planar Uniformity Index (PUI) and Vertical Uniformity Index (VUI) with a terrain-adaptive weighting mechanism. The VUI weight, calibrated as β=0.714 via a Sigmoid nonlinear amplification function (k=15,x0=0.15), enables the SDBI to adaptively reflect terrain sensitivity to vertical control. The enhanced SDBI shows a correlation coefficient of r=−0.93 with final accuracy, validating its effectiveness as a GCP layout optimization and evaluation tool. This research provides a quantifiable technical framework for GCP deployment in UAV photogrammetry in hilly regions.

Abstract: Fractional logistic maps introduce memory into a canonical route to chaos, but how memory shifts successive period-doubling bifurcation points is not yet fully understood. For the finite-memory Grünwald–Letnikov (GL) fractional logistic map, we formulate equations that determine exact periodic orbits and their period-doubling points, with the number of unknowns independent of memory length. The framework reproduces an independently derived exact fixed-point boundary and identifies a genuine period-4 to period-8 doubling, confirming its applicability beyond the fixed point. Across the first seven computed bifurcation levels, increasing memory length shifts the bifurcation points toward smaller control-parameter values, with stronger shifts at lower fractional orders. The GL memory tail decays more slowly at lower fractional order, helping explain why extending retained memory produces larger bifurcation-point shifts. At fixed memory length, decreasing the fractional order from its classical value first moves the bifurcation points toward smaller control-parameter values, before a turnover carries them toward larger values. As the order approaches zero, the GL memory terms vanish and limiting bifurcation points lie one unit above their classical counterparts, so branch continuity requires a turnover. Together, these results provide a validated basis for computing and interpreting how memory reorganizes the period-doubling route to chaos.

Abstract: Objective: To assess gut and endometrial microbiota simultaneously in women with unexplained recurrent implantation failure (RIF) and explore the hypothesis of a gut-endometrium microbial axis. Study design: This monocentric observational pilot study included women aged 38 years or younger with RIF and a normal uterine cavity. Paired fecal and endometrial samples were collected on the same day and analyzed by 16S rRNA gene sequencing. Relative abundances of major phyla and alpha-diversity indices were compared between compartments. Results: Twenty-one women were included. Chronic endometritis was documented hysteroscopically in all cases. Gut dysbiosis was observed in 20/21 patients (95.2%), whereas endometrial dysbiosis was found in 16/21 (76.2%); crude agreement between compartments was 71.4%. The intestinal microbiota was dominated by Bacteroidetes (52.8% ± 14.9%) and Firmicutes (33.3% ± 11.0%), whereas the endometrial microbiota was enriched in Firmicutes (64.5% ± 33.5%) and Proteobacteria (30.9% ± 32.3%). Bacteroidetes were significantly more abundant in fecal than endometrial samples (p < 0.001), while Firmicutes were significantly enriched in the endometrium (p = 0.001). All alpha-diversity indices were significantly higher in feces, indicating greater microbial richness and complexity in the gut. Conclusions: In women with RIF, gut and endometrial dysbiosis frequently coexist and show relevant concordance. These data support the hypothesis that endometrial dysbiosis may reflect a broader systemic microbial imbalance rather than an isolated uterine disorder.

Abstract: Background/Objectives: Plexiform neurofibromas (PNs) associated with Neurofibromatosis type 1 (NF1) can lead to spinal cord compression and motor dysfunction. However, there is a paucity of literature detailing the effects of selumetinib on dystonia or spastic gait in these patients. Methods: A retrospective analysis was conducted on a pediatric patient with NF1-associated PN, dystonia, and spastic gait who received selumetinib treatment. Additionally, two cases from Chinese-language literature were reviewed, sourced from the CNKI, Wanfang, and VIP databases. Results: Three pediatric cases were identified. All patients presented with café-au-lait macules, superficial nodules, and either gait disturbance or dystonia; two exhibited scoliosis, and all demonstrated both intra- and extraspinal PN involvement. Following selumetinib therapy, there was a reduction in tumor burden and an improvement in gait-related motor symptoms. In the case under review, tiptoe walking and frequent falls showed significant improvement after 180 days of treatment, with no adverse events reported during the follow-up period. Conclusions: Selumetinib appears to be a promising therapeutic option for children with symptomatic, unresectable NF1-associated PNs and compression-related dystonia or spastic gait. Nonetheless, further longitudinal studies and additional case reports are necessary to substantiate these findings.

Abstract: Alopecia is an emerging concern during tirzepatide or semaglutide therapy for weight management, but whether it is a consequence of weight loss or reflects incretin-specific biology remains unclear. Here we conduct an observational study comparing new users of tirzepatide (Zepbound, Mounjaro) or semaglutide (Wegovy, Ozempic, Rybelsus) with at least two prescriptions, no documented alopecia prior to the first prescription, and complete 12-month follow-up. Incident alopecia was defined using harmonized diagnosis codes and AI-curated clinical notes. Propensity-score matching was performed to balance tirzepatide and semaglutide users on age, race, sex, baseline BMI, and type 2 diabetes. In the matched cohorts (n=12,863 per arm), incident alopecia was more frequent with tirzepatide than with semaglutide (4.20% vs 2.88%; risk ratio [RR]: 1.46; 95% CI [1.28-1.66]; P<0.001). After additional matching on achieved weight loss (n=11,046 per arm), incident alopecia remained significantly higher after tirzepatide than semaglutide (4.24% versus 3.33%; RR: 1.27 [1.11-1.45]; P<0.001). By 6 months, incident alopecia had occurred in 1.55% of tirzepatide users versus 1.24% of semaglutide users, widening by 12 months to 4.20% versus 2.88%, respectively (hazard ratio [HR]: 1.46 [1.28-1.67]; log-rank P<0.001). Incident alopecia remained significantly more frequent with tirzepatide than semaglutide across clinically relevant strata (all P<0.001), including among patients with 20-to-30% weight loss, a range highlighted in pivotal obesity trials (RR: 1.44); in the low and high maximum-dose strata (RR: 1.76 and 1.39, respectively); and among patients with 4-6 prescriptions, a proxy for longer treatment duration (RR: 1.65). Analysis of newly initiated hair-loss therapies showed significantly higher minoxidil (Rogaine) initiation after tirzepatide than after semaglutide (RR, 2.04; P=0.002). Among 8,985 female patients, incident alopecia occurred in 5.44% of tirzepatide users versus 3.63% of semaglutide users (RR: 1.50 [1.31-1.72]; P<0.001), with significant differences in the 10-to-20% weight-loss band (RR: 1.41 [1.11-1.78]; P=0.004) and in the 20-to-30% weight-loss band (RR, 1.47; 95% CI, 1.06 to 2.04; P=0.018). Among tirzepatide users, incident-alopecia developers were more often female than non-developers (90.6% vs 68.9%, SMD: +0.56), and were enriched for underlying endocrine conditions including menstrual irregularity (20.0% vs 13.9%, SMD: +0.17), hypothyroidism (28.1% vs 21.6%, SMD: +0.15), and polycystic ovarian syndrome (6.7% vs 3.6%, SMD: +0.14). Repeated measurements of ferritin, iron, vitamin B12, folate, vitamin D, and zinc showed no significant nutrient decline accompanying incident alopecia. Exploratory single-cell RNA-seq analyses showed no GLP1R or GIPR expression in scalp follicular keratinocytes and identified multiple dermato-immune cell types that expressed GIPR but not GLP1R. Overall, in routine care, incident alopecia was higher after initiation of tirzepatide than semaglutide, motivating prospective comparative studies of incretin-based therapies.

Abstract: This study aims to forecast South Africa’s import demand for cassava starch using monthly data from April 2015 to December 2030. It applies Augmented Dickey-Fuller (ADF) to verify the stationarity of the series, while the Akaike criterion and Schwarz criterion were utilized to select the fitted model of ARIMA (p, d, q). The series were integrated of order one, and the best-fitted model was ARIMA (0,1,2). Thereafter, the Box-Jenkins estimation strategy was used to forecast the import demand for cassava starch. The forecasted import demand for cassava starch showed a steady increase in quantity, with an annual average import demand of 12 032 tons between 2025 and 2030. Given South Africa’s rising cassava starch imports, we endeavored to forecast demand thereof, considering the absence of commercial cassava production and processing in the country. This is the first empirical study to offer a thorough forecasting of the rising trend in Southern Africa’s demand for cassava starch imports. To manage the growing demand for cassava starch in South Africa, policymakers should consider medium- to long-term interventions to further develop the value chain. The most urgent and critical need is to fund research and development (R&D) for the advancement of the cassava value chain, as well as to establish phytosanitary-certified nurseries for producers to access high-quality planting materials.

Abstract: Currently, several LLMs (Large Language Models) assist users with specific tasks, such as summarizing texts or solving physics problems, to name a few. On the other hand, using scripts allows users to automate tasks and save time in their creation. This article evaluates an LLM for generating scripts for the Application Programming Interface (API) of the Fusion 360 CAD software application, to determine its capabilities for developing 3D modeling components used in the simulation of industrial robots or the automation of manufacturing systems. Among the results, it was found that the generated scripts enable modeling of industrial robots and automation elements using simple geometries that the user can later customize for a specific application. Furthermore, it is recommended that users be familiar with Fusion 360 in case they need to modify or assemble the components created by scripting. At the end, a Prompt Engineering Guide for Fusion 360 scripts focused on generating components for industrial robots is shared.

Abstract: Background: To evaluate the sociodemographic and clinical profiles of pregnant women with positive IgM anti‑T. gondii in the eastern Brazilian Legal Amazon, during the period from 2018 to 2023. Methods: This was an indirect observational study through the retrospective analysis of 1,049 medical records from pregnant women attended in the Obstetrics service of Basic Health Units. The dependent variable was the presence of IgM antibodies, and the independent variables included age brackets, ethnicity, schooling, occupation, and clinical complaints. Data were analyzed using Epi‑Info 3.3.2 software. Risk factors and odds ratio (OR) with 95% CI were calculated. Results: From the 1,049 medical records analyzed, 52 (4.97%) presented IgM anti‑T. gondii antibodies. There was a significant correlation between IgM presence and age older than 30 years (OR 2.17; CI: 1.60–7.12), being employed (OR: 8.42; CI: 1.05–22.12), being married (OR: 3.02; CI: 1.43–9.32), and having less than eight years of schooling (OR: 3.04; CI: 1.24–11.56). Conclusions: The prevalence of IgM antibodies in the studied population was considered high, associated with low schooling, employment, and age over 30 years. The asymptomatic characteristic of acute infection is highlighted, reinforcing the need for effective prenatal screening.