Lehmann Eye Center

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.

Background: Artificial intelligence is emerging as a promising tool in surgical oncology, with growing evidence suggesting potential applications in diagnostic support, intraoperative guidance, and perioperative risk assessment. In gastric cancer surgery, emerging applications range from AI-assisted endoscopic detection to data-driven perioperative risk prediction, while some technological developments, particularly in robotic autonomy, derive from broader surgical or experimental models that may inform future gastric procedures. Methods: A narrative review was conducted following established methodological standards, including the Scale for the Assessment of Narrative Review Articles (SANRA) and the Search-Appraisal-Synthesis-Analysis (SALSA) framework. English-language studies indexed in PubMed, Scopus, Embase, and Web of Science up to October 2025 were included. Evidence was synthesized thematically across five domains: AI-assisted anatomical recognition and lymphadenectomy support, autonomous robotic systems, early cancer detection, perioperative predictive and frailty models, and ethical and regulatory considerations. Results: AI-based computer vision and deep learning algorithms have demonstrated promising capabilities for real-time anatomical recognition, surgical phase classification, and intraoperative guidance, although evidence of direct patient-level benefit remains limited. In diagnostic settings, AI-assisted endoscopy and Raman spectroscopy have been shown to improve early lesion detection and reduce dependence on operator experience. Predictive models, including MySurgeryRisk and AI-driven frailty assessments, may support individualized prehabilitation planning and perioperative risk stratification. Persistent limitations include small and heterogeneous datasets, insufficient external validation, and unresolved concerns related to data privacy, algorithmic interpretability, and medico-legal responsibility. Conclusions: Artificial intelligence is progressively emerging as a promising tool in gastric cancer surgery, integrating automation, advanced analytics, and human clinical reasoning. Its safe and ethical adoption requires robust validation, transparent governance, and continuous surgeon oversight. When developed within human-centered and ethically grounded frameworks, AI can augment, rather than replace, surgical expertise, potentially advancing precision, safety, and equity in oncologic care.

Roux-en-Y gastric bypass (RYGB) is a widely performed and well-established metabolic and bariatric surgery (MBS) procedure around the world based on its high efficacy and low complication profile when offered to appropriately selected candidates. However, it is still associated with chronic abdominal pain confounded by nonspecific symptoms and signs that many times lead to a delay in diagnosis or excessive and unnecessary testing that may put patients at risk for long-term complications and adverse events. Among the most important causes of chronic abdominal pain after RYGB are marginal ulcers, intussusception, internal hernias, bile reflux gastritis, dumping syndrome, small intestinal bacterial overgrowth (SIBO), candy cane syndrome, biliary pathology, vascular etiologies, and gastroesophageal reflux disease (GERD). A comprehensive literature review based on the highest-level available published manuscripts has been conducted with the goal of providing the readers and members from the American Society for Metabolic and Bariatric Surgery (ASMBS) with a practical algorithm to diagnose and treat chronic abdominal pain after RYGB. An organized approach that promotes early diagnosis and cause-specific management in a multidisciplinary team environment led by metabolic and bariatric surgeons is advised and encouraged. This literature review by the members of the Clinical Issues Committee (CIC) of ASMBS introduces available high-level evidence to allow our members to achieve early diagnosis of the etiologies of chronic abdominal pain and the expedited therapeutic maneuvers to address them and improve quality of life while increasing patient survival.

Background: Gastric cancer (GC) represents a significant global health burden with considerable heterogeneity in clinical and molecular behavior. The anatomical site of tumor origin-proximal versus distal-has emerged as a determinant of prognosis and response to therapy. The aim of this paper is to elucidate the transcriptional and regulatory differences between proximal gastric cancer (PGC) and distal gastric cancer (DGC) through master regulator (MR) analysis. Methods: We analyzed RNA-seq data from TCGA-STAD and microarray data from GEO (GSE62254, GSE15459). Differential gene expression and MR analyses were performed using DESeq2, limma, corto, and RegEnrich pipelines. A harmonized matrix of 4785 genes was used for MR inference following normalization and batch correction. Functional enrichment and survival analyses were conducted to explore prognostic associations. Results: Among 364 TCGA and 492 GEO patients, PGC was associated with more aggressive clinicopathological features and poorer outcomes. We identified 998 DEGs distinguishing PGC and DGC. PGC showed increased FOXM1 (a key regulator of cell proliferation), STAT3, and NF-κB1 activity, while DGC displayed enriched GATA6, CDX2 (a marker of intestinal differentiation), and HNF4A signaling. Functional enrichment highlighted proliferative and inflammatory programs in PGC, and differentiation and metabolic pathways in DGC. MR activity stratified survival outcomes, reinforcing prognostic relevance. Conclusions: PGC and DGC are governed by distinct transcriptional regulators and signaling networks. Our findings provide a biological rationale for location-based stratification and inform targeted therapy development.

Predictive modeling for metastasis in oncology has gained significant traction due to its potential to improve prognosis, guide treatment strategies and enhance patient outcomes. Current methods leverage advancements in machine learning, genomics and imaging technologies to predict the likelihood of cancer spread. Techniques such as logistic regression, decision trees, support vector machines and neural networks have been employed to analyze clinical, pathological, and molecular data. Genomic profiling, liquid biopsies, and radiomics are increasingly integrated into these models to identify metastatic patterns and risk factors. Despite these advances, challenges persist, including data heterogeneity, model interpretability, and the need for larger, high-quality datasets for validation. Furthermore, the integration of artificial intelligence with precision medicine offers promising avenues for more personalized metastasis predictions. Future directions focus on enhancing model accuracy through deep learning, improving the interpretability of black-box models, and incorporating multi-omics data to capture the complexity of metastatic mechanisms. With the advent of advanced computational tools and growing datasets, predictive modeling in oncology is poised to revolutionize metastasis management, offering clinicians’ valuable insights for early detection and tailored treatment strategies.

The approval of resmetirom brings great hope to patients with metabolic dysfunction-associated steatohepatitis (MASH). The purpose of this review is to explore its impact on the global health environment. The implementation of multidisciplinary management MASH is proposed. Resmetirom has benefits in the treatment of MASH, and its safety and effectiveness have been studied. The adverse events (AEs) need to be noticed. To improve patient outcomes, a multimodal approach with medication such as resmetirom, combined with metabolic and bariatric surgery (MBS) and lifestyle interventions can be conducted. MASH, a liver disease linked with obesity, is a challenging global healthcare burden compounded by the absence of any approved pharmacotherapy. The recent conditional approval by the Food and Drug Administration(FDA) in the United States of resmetirom, an oral, liver-directed, thyroid hormone receptor beta-selective agonist, marks a significant milestone, offering a treatment option for adults with non-cirrhotic MASH and who have moderate to advanced liver fibrosis. This narrative review discusses the efficacy and safety of resmetirom and its role in the therapeutic landscape of MASH treatment. Despite the promising hepatoprotective effect of resmetirom on histological liver endpoints, its use need further research, particularly regarding ethnic differences, effectiveness and cost-effectiveness, production scalability, social acceptance and accessibility. In addition, integrating resmetirom with other multidisciplinary therapeutic approaches, including lifestyle changes and MBS, might further improve clinical liver-related and cardiometabolic outcomes of individuals with MASH. This review highlights the importance of a comprehensive treatment strategy, supporting continued innovation and collaborative research to refine treatment guidelines and consensus for managing MASH, thereby improving clinical patient outcomes in the growing global epidemic of MASH. Studies done to date have been relatively short and ongoing, the course of the disease is highly variable, the conditions of various patients vary, and given this complex clinical phenotype, it may take many years of clinical trials to show long-term benefits.

The rise of robotic surgery throughout the world, particularly in Latin America, justifies an objective evaluation of research in this field. This study aimed to use bibliometric techniques to identify the research trends and patterns of robotic surgery in Latin America. The research strategy used the terms "Robotic," "Surgery," and the name of all the Latin American countries, in all fields and collections of Web of Science database. Only original articles published between 2009 and 2022 were included. The software Rayyan, Bibliometric in the R Studio, and VOSViewer were used to develop the analyses. After screening, 96 articles were included from 60 different journals. There was a 22.51% annual increase in the scientific production of robotic surgery in the period studied. The more frequent topics by specialty were: Urology (35.4%), General Surgery (34.4%), and Obstetrics and Gynecology (12%). International cooperation was observed in 65.62% of the studies. The Latin American institution with the highest production of manuscripts was the Pontificia Universidad Católica de Chile. Mexico, Chile, and Brazil were, in descending order, the nations with the highest number of corresponding authors and total citations. When considering the total number of articles, Brazil ranked ahead of Chile. Scientific production regarding robotic surgery in Latin America has experienced accelerated growth since its beginning, supported by the high degree of collaboration with leading countries in the field.

Pulse radiolysis (PR) enables the full redox window of a solvent to be accessed, as it does not require electrodes or electrolyte which limit the potentials accessible in voltammetry measurements. PR in chloroform has the additional possibility to enable reaching highly positive potentials because of its large ionization potential (IP). PR experiments demonstrated the formation of the (deuterated) chloroform radical cation CDCl3+˙, identifying it as the source of the broad absorption in the visible part of the spectrum. Results indicated that solutes with a redox potential up to +3.7 V vs. Fc/Fc+ can be oxidized by CDCl3+˙, which is far beyond what is possible with electrochemical techniques. Oxidation is not efficient because of rapid geminate recombination with chloride counterions, but also due to rapid decomposition of CDCl3+˙ which limits the yield of otherwise longer-lived free ions. The rapid, 6 ± 3 ns, decomposition, confirmed by two independent experiments, means that a solute must be present at a concentration >100 mM to capture >90% of the free holes formed. Addition of ethene removes the broad, overlapping absorptions from ubiquitous (chlorine atom, solute) complexes created by PR in halogenated solvents enabling clear observation of solute cations. The results also unravel the complex radiation chemistry of chloroform including the large reported value G(-CHCl3) = 12 molecules/100 eV for the decomposition of chloroform molecules.