Metal allergies have been a growing concern in the general population over the past several decades. Laparoscopic cholecystectomy is the standard treatment for gallbladder diseases such as cholelithiasis and cholecystitis, during which surgical clips composed of metals such as nickel or titanium are often used to clamp the arteries and ducts. These metals are documented to produce hypersensitivity reactions. Here, we present the case of a 53-year-old male patient who successfully underwent laparoscopic cholecystectomy and two weeks later reported constant right upper quadrant pain accompanied by nausea that was exacerbated by exercise and food. After several months of severe interference with the patient’s lifestyle, we removed the surgical clips after ruling out all possible organic causes of the pain. A total of six surgical clips were removed during surgery, and the patient reported a substantial resolution of symptoms post-operation. Post-cholecystectomy syndrome (PCS), allergy to the metallic surgical clips, and migration or improper clip placement during surgery were all considered possible causes for the pain. Still, the clinical presentation and laboratory studies pulled focus toward metallic surgical clip allergy as the most plausible cause for the presenting symptoms. The metallic haptens released by the surgical clips activate the innate and adaptive immune response cascades and pre-sensitize the CD8 and B cells to the metallic allergens. With reexposure, these pre-sensitized CD8 and B cells trigger a hypersensitivity reaction. Standardizing allergy tests as part of the pre-operation checklist can prove to be an inexpensive way to eliminate such post-surgical complications. Furthermore, alternatives like absorbable sutures or even different hypoallergenic metal or plastic clips can be considered viable options to replace nickel or titanium-made surgical clips during surgery.

Background: AI research increasingly depends on energy-intensive computation, yet energy use, greenhouse gas emissions, hardware life cycle burdens, and water consumption are rarely reported in a standardized way. This lack of reproducible environmental accounting limits comparisons across studies and obscures the trade-offs among model performance, infrastructure choices, carbon intensity, and cooling water demand. Objective: This study aimed to develop and describe an open-access, life cycle–informed AI Environmental Footprint Calculator and propose a minimum reporting dataset for transparent environmental disclosure in AI research. Methods: We developed a browser-based calculator that combines operational energy, regional grid carbon intensity, power usage effectiveness (PUE), water usage effectiveness, hardware embodied emissions, and workload-specific metrics for training and inference. The framework was evaluated in 3 representative scenarios: a single–graphics processing unit laboratory fine-tuning task, a midsized academic cluster workload, and a large-scale industrial training cycle. Outputs were compared with those of established tools to identify how boundary choices and parameter assumptions affect emission estimates. Results: Across the scenarios, the inclusion of PUE and hardware life cycle allocation increased reported emissions compared with operational-only estimates. In the small laboratory scenario, optimization reduced total emissions from approximately 0.07 kg carbon dioxide equivalents (CO ₂ e) to 0.05 kg CO ₂ e and improved the proposed label from C to B. In the midsized cluster scenario, carbon-aware scheduling reduced emissions from approximately 665 kg CO ₂ e/month to 450 kg CO ₂ e/month—a 32% reduction. In the large-scale scenario, shifting to renewable-backed, lower-PUE infrastructure reduced operational emissions by approximately 79% while increasing the importance of water-carbon trade-off reporting. Conclusions: The calculator provides a practical and transparent method for reporting AI environmental footprints using auditable parameters and publication-ready outputs. Routine disclosure of energy use, emissions, water use, hardware assumptions, and regional context can improve reproducibility and support more equitable and sustainable AI research evaluation.

Inguinal hernias are the most common type of hernias, and most are repaired surgically with mesh placement to prevent future reoccurrence. Mesh infection and hernia recurrence are some of the rare complications of mesh placement, and chronic mesh infections increase the risk of squamous cell carcinoma at the site. Squamous cell carcinoma (SCC) in the setting of a mesh infection presents in a similar manner to a Marjolin ulcer and is treated with the removal of the tumor and degraded infected mesh. However, in this case, the patient presented atypically with an absence of mesh involvement. This report aims to explore the etiology of SCC due to mesh infections as well as describe the perplexing case of inguinal SCC in the absence of mesh involvement.

Acute appendicitis, a typical abdominal emergency, presents in nonperforated and perforated forms. While nonperforated cases often respond well to antibiotics or appendectomy, perforated cases pose more significant challenges. We report a unique case of appendicitis in a 34-year-old female with an unusual anatomical adherence to the left ovary. The patient successfully underwent laparoscopic surgery, during which precise incisions were made to accommodate the unique location of the appendix. This tailored approach contributed to a positive surgical outcome. This case underscores the importance of tailored surgical approaches in managing complex appendiceal variations.

BACKGROUND Artificial intelligence (AI) systems increasingly contribute to global energy consumption, greenhouse gas emissions, and water use, yet these impacts remain inconsistently measured and poorly reported in the research literature. Existing tools primarily focus on operational energy during model training and often omit critical contributors, including hardware lifecycle emissions, cooling overhead, and inference-level impacts. The absence of standardized, accessible, and reproducible environmental accounting frameworks limits comparability across studies and impedes environmental transparency in AI research. OBJECTIVE This study aims to develop and validate a transparent, lifecycle-informed framework for quantifying and reporting the environmental footprint of AI workloads and to propose a standardized environmental reporting and labeling approach suitable for routine inclusion in AI research publications. METHODS We developed the AI Environmental Footprint Calculator, a browser-based, open-access tool that integrates four core components of environmental accounting: (1) operational energy use derived from hardware power and runtime; (2) embodied emissions from hardware manufacturing and end-of-life amortized over device lifetime; (3) cooling and infrastructure overhead using configurable Power Usage Effectiveness (PUE) and optional Water Usage Effectiveness (WUE); and (4) inference-level granularity expressed as carbon dioxide equivalent (CO₂e) per 1,000 tokens or per request. The calculator incorporates regional and time-resolved grid carbon-intensity data and provides both minimal-input and expert modes. Validation was performed using three representative scenarios: a small laboratory fine-tuning task, a mid-sized academic research cluster, and a large-scale industrial training workload. Sensitivity analyses assessed the influence of key parameters, including grid emission factor, PUE, and hardware utilization. RESULTS Across validation scenarios, the calculator produced consistent and interpretable estimates of energy use, CO₂e emissions, and water consumption, while revealing systematic underestimation by operational-only tools. Inclusion of hardware lifecycle and cooling effects increased total emissions estimates by approximately 20%–40%, depending on workload scale. Scenario analyses demonstrated that algorithmic optimization, carbon-aware scheduling, and infrastructure choices can substantially reduce environmental impact. Based on these outputs, we introduce an AI Environmental Label (A–E grade) that benchmarks workloads using normalized carbon-intensity metrics for training and inference, enabling intuitive comparison across studies. CONCLUSIONS This work presents a comprehensive, reproducible framework for environmental accounting in AI research that bridges operational measurement with lifecycle assessment and inference-level reporting. By combining methodological rigor with accessibility, the proposed tool and labeling system support standardized environmental disclosure and informed decision-making. Adoption of such frameworks can facilitate transparency, comparability, and responsible scaling of AI systems across research environments.