Sam Houston State University

Inguinal hernia repair is one of the most common surgical procedures performed globally. Laparoscopic inguinal hernia repair (LIHR) is recognized globally to be effective and safe, with advantages over open surgery, but its implementation across the African continent has been slow, with only 12 countries reporting implementation, and only 3.3% of inguinal hernia repairs in sub-Saharan Africa performed using laparoscopic techniques. Robotic surgery, although still emerging within the continent, with around 20 robots primarily used in urology across South Africa, Egypt, Morocco, Angola, and Tunisia, no reports of robotic inguinal hernia repair currently exist. Progress, however, is being observed with the growing interest from surgical societies, private-sector robotic expansion, and humanitarian missions introducing mesh-based and limited laparoscopic procedures. Limitations include the low global utilization of minimally invasive surgery (MIS) for hernia repair despite guideline recommendations. This has been attributed to training challenges, steep learning curve, and limited evidence of benefit for bilateral and recurrent hernias. African-specific challenges include costs, inadequate training opportunities, surgeon preference, ongoing debates regarding its necessity in low-resource settings, lack of institutional support, and resource prioritization for other MIS procedures such as cholecystectomy and prostatectomy. Despite ongoing challenges, investments in research, training and cost-effective equipment, increased availability of mesh, and integration of humanitarian hernia missions into national training systems, can enhance adoption and contribute to better surgical outcomes for patients. This narrative review presents the present state of robotic and laparoscopic inguinal hernia repair in Africa, as well as the current challenges, and recommendations to improve adoption.

In 2018, the National Institutes of Health (NIH) launched the Helping to End Addiction Long-Term ® (HEAL) Initiative to advance addiction and pain research and clinical practice. The NIH-HEAL Initiative supports investigator-initiated research across diverse focus areas, allowing scientists discretion in selecting research topics, methodologies, and outcomes while still remaining subject to NIH's peer review process. This study aims to answer the research question: what do investigators of NIH-HEAL funded studies define as important knowledge, particularly in relation to growth in empirical studies and clinical policy? By examining how pain and addiction researchers define what knowledge is important to produce, this study offers novel insight into how research priorities take shape and why this matters for science and clinical practice. To answer this research question, we conducted a directed content analysis of 1,068 NIH-HEAL funded study abstracts published on the NIH RePORTER website. Our findings suggest that while the scope of the NIH-HEAL funded studies is broad, there are common identifiable patterns that exist within the funded research portfolio such as a preference for treating pain and addiction conditions with prescription medication (33.80% of pain studies, 34.17% of opioid use disorder studies) and a narrow focus on intervention efficacy (43.21% of pain studies, 39.22% of opioid use disorder studies). This analysis is important for the scientific enterprise and clinical practice as prior literature has painted research as inherently influenced by the values of a scientist and of their discipline. By identifying what social and empirical problems NIH-HEAL funded investigators identify, their proposed solutions, and the outcomes they prioritize, this study carries important implications for grant-funding agencies, scientists, and the people who need and receive treatment for pain and addiction conditions.

ABSTRACT The 2024 presidential election unfolded under extraordinary circumstances—the mid‐campaign withdrawal of an incumbent president, the nomination of the first woman of color by a major party, and the reelection of a president defeated 4 years earlier. Yet the outcome revealed an electorate that looked much like 2016 and 2020. Drawing on ANES, CES, and exit poll data, we argue that the Trump era has produced a “stuck” electorate that is deeply polarized, highly nationalized, and resistant to change. Race remains the most durable cleavage, with Democrats reliant on African Americans and Republicans dependent on whites. Latinos and Asians shifted sharply toward the GOP, while gender, education, and income reinforced enduring divisions. Trump's enduring brand crystallized these alignments and nationalized electoral competition, reducing local distinctiveness and constraining opportunities for realignment. The Trump era, we conclude, is best understood not as a period of partisan transformation but one of consolidation, with important implications for 2028 and beyond.

This study evaluated the performance and limitations of the ForenSeq™ MainstAY chemistry on the MiSeq FGx® Sequencing System, which is approved for National DNA Index System (NDIS) use. This study included baseline performance, sensitivity and reproducibility studies, challenging casework samples, assessments of library plexities, comparisons with capillary electrophoresis (CE), standard versus enhanced PCR1 buffers, manual versus automated library preparation, and different flow cell types. To assess reproducibility across users, 33 samples were prepared by both an experienced and a novice user. Overall, the ForenSeq™ MainstAY Kit demonstrated high reliability with pristine samples and consistent performance across users, with minimal locus dropout until 62 pg. The kit outperformed CE for low-input and degraded samples, but limitations were observed at higher plexities. Increased sample numbers on a single flow cell disproportionately reduced read counts and locus recovery in degraded casework samples, while pristine samples were less affected. Strategic adjustments, such as batching degraded samples together or lowering plexity, may help mitigate these effects. Casework samples, including blood, touched items, bone, hair, and tissue, were tested with both standard and enhanced PCR1 buffers. In a 64-sample run (32 per buffer type), increased multiplexing reduced locus recovery in standard buffer samples, while the enhanced buffer improved recovery in 16 samples. Automated library preparation on the Opentrons OT-2 produced comparable or improved recovery relative to manual preparation, with no evidence of cross-contamination. These findings provide practical guidance for forensic laboratories adopting the MainstAY Kit, particularly in optimizing workflows for challenging samples and high-throughput sequencing environments.

ABSTRACT Social media has become a prominent platform for political discourse, with celebrity endorsements playing a crucial role in political communication. Both significantly influence political campaigns and public opinion on political issues. A key factor in this impact is how a celebrity’s political message is perceived by their audience. This research explores whether a celebrity’s political speech affects how their audience perceives the use of their celebrity status and their place in political conversations. The study employs an experimental design, utilising the theoretical framework of source characteristics to examine individuals’ attitudes towards political information shared by celebrities. The findings reveal that when a celebrity’s political message conflicts with an individual’s views, the celebrity is perceived as having lower credibility, with audiences often believing that the celebrity should ‘stick to what they [celebrities] know’ best – entertainment and not politics. Conversely, when a celebrity’s political stance aligns with an individual’s views, they are more welcomed to discuss politics. Additionally, the study examines parasocial relationships, finding that stronger parasocial connections enhance the perceived credibility of the celebrity. This research underscores the complex dynamics between celebrity influence, audience perception, and political communication.

COVID-19 exposed longstanding racial and ethnic inequities among underserved populations. This retrospective cohort study examined inequities across stages of the hospitalization continuum-from COVID-19 diagnosis at admission to in-hospital mortality, including mortality patterns among COVID-19 hospitalizations-among Medicaid beneficiaries in Kentucky during 2020-2021. Statewide hospitalizations were analyzed using multivariable regression models, with propensity score matching (PSM) used as a confirmatory approach. Non-Hispanic Black patients were more likely than non-Hispanic White patients to be hospitalized with COVID-19 (adjusted odds ratio [aOR] = 1.41; 95% confidence interval [CI] = 1.26-1.59). Across the full cohort, COVID-19 hospitalizations were associated with substantially higher in-hospital mortality compared with non-COVID-19 hospitalizations (adjusted hazard ratio [aHR] = 2.38; 95% CI = 2.09-2.70). Additionally, hospitalizations among non-Hispanic Black patients had a modestly lower hazard of in-hospital mortality compared with non-Hispanic White patients (aHR = 0.81; 95% CI = 0.70-0.94). However, in analyses restricted to COVID-19 hospitalizations, adjusted estimates showed no Black-White differences in in-hospital mortality, with consistent findings from PSM analyses. These results indicate that racial inequities were more pronounced at hospital admission than during inpatient care, underscoring the importance of prevention, early diagnosis, and timely outpatient care as COVID-19 enters an endemic phase.

<strong class="journal-contentHeaderColor">Abstract.</strong> Arctic rivers represent important components of the Arctic and global hydrological and climate systems, serving as dynamic conduits between terrestrial and marine environments in some rapidly changing regions. They transport freshwater, sediments, nutrients, and carbon from vast watersheds to the Arctic Ocean and affect ocean circulation patterns and regional climate dynamics. Despite their importance, modeling Arctic rivers remains challenging because of sparse data networks, unique cryospheric dynamics, and complex responses to hydrometeorological variables. In this study, a novel hybrid deep learning model is developed to address these challenges and predict Arctic River discharge by incorporating Kolmogorov-Arnold Networks (KAN), Long Short-Term Memory, and the attention mechanism with seasonal trigonometry encoding and physics-based constrains. It integrates several novel components: 1) The KAN-based deep learning component learns and captures intricate temporal patterns from nonlinear hydrometeorological data; 2) Explicit physical constrains designed for the characteristics of permafrost-dominated watersheds govern snow accumulation and melt processes through the architectural design and loss function; 3) The seasonal variations are accounted for using trigonometry functions to represent cyclical patterns; 4) A residual compensation stricture allows the proposed model to revisit systematic errors in initial predictions and helps capture complex nonlinear processes that are not fully represented. The Kolyma River, which is significantly dominated by the permafrost, is adopted to test the performance of the newly developed model. It obtains more robust and accurate predictive performance compared to baseline models. The role of physical constraints, the residual compensated architecture, and the trigonometry encoding are assessed by ablation analysis. The results indicate that these components positively contribute to improving the predictive performance. This novel approach addresses the unique challenges of hydrological forecasting in cold, permafrost-dominated regions and provides a robust framework for predicting Arctic River discharge under changing climate conditions.