United States

While professional identity formation is recognized as beneficial for medical students’ well-being and relationships with colleagues and patients, students consistently report challenges with engaging in meaningful respite and reflection. Time spent in nature has been repeatedly shown to support well-being, reduce stress, and enhance reflective capacity, yet remains to be used in a reflective practice for students. In contrast, lack of contact with nature is associated with higher levels of anxiety, depression, decreased creativity, attention difficulties, and decreased overall physical and emotional well-being. This study explores medical students’ reflections on important formative connections to nature and the barriers that limited these connections. 

We collected data from 56 medical students from across the United States who participated in the Backpacking Medicine course offered by the Advanced Wilderness Life Support (AWLS) program at the University of Utah School of Medicine. Participants were divided into four focus groups and asked to write on a notecard their favorite memory in nature and a personal barrier to accessing nature. These reflections were discussed within the groups in relation to students’ current experiences in medical school and their future roles as physicians. The written responses were analyzed using thematic analysis, with codes organized into themes and subthemes. 

Three major themes emerged for students’ favorite memory: Family &amp; Social Bonds, Independent Play &amp; Exploration, and Structured Outdoor Programs &amp; Skill Building. Six major themes emerged for students’ barriers to outdoor access: Health &amp; Physical Limitations, Negative or Traumatic Outdoor Experiences, Lack of Social Support or Modeling, Environmental and Structural Access Barriers, Cultural and Generational Barriers, Time &amp; Life Demands. 

Our findings highlight the role of nature exposure in promoting reflective practices, identity development, and well-being in medical students. Students’ favorite outdoor memories emphasized the value of social connection, skill development, and personal growth. Reflections on barriers to the outdoors prompted students to consider both their own access to nature and that of future patients, inspiring creative thinking around the idea of “prescribing” nature in clinical care. A key limitation of this study is sample bias, as participants signed up for this nature-based course and may have held prior interest in or appreciation for the benefits of outdoor experiences. Future research is needed to determine the long-term effects of implementing nature as a reflective tool in medical education. 





AMA Research Challenge 2025 

Exploring the Use of Nature as a Reflective Tool in Medical Education

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University of Victoria and College Partners BSN Virtual Conference

While professional identity formation is recognized as beneficial for medical students’ well-being and relationships with colleagues and patients, students consistently report challenges with engaging in meaningful respite and reflection. Time spent in nature has been repeatedly shown to support well-being, reduce stress, and enhance reflective capacity, yet remains to be used in a reflective practice for students. In contrast, lack of contact with nature is associated with higher levels of anxiety, depression, decreased creativity, attention difficulties, and decreased overall physical and emotional well-being. This study explores medical students’ reflections on important formative connections to nature and the barriers that limited these connections. 

We collected data from 56 medical students from across the United States who participated in the Backpacking Medicine course offered by the Advanced Wilderness Life Support (AWLS) program at the University of Utah School of Medicine. Participants were divided into four focus groups and asked to write on a notecard their favorite memory in nature and a personal barrier to accessing nature. These reflections were discussed within the groups in relation to students’ current experiences in medical school and their future roles as physicians. The written responses were analyzed using thematic analysis, with codes organized into themes and subthemes. 

Three major themes emerged for students’ favorite memory: Family & Social Bonds, Independent Play & Exploration, and Structured Outdoor Programs & Skill Building. Six major themes emerged for students’ barriers to outdoor access: Health & Physical Limitations, Negative or Traumatic Outdoor Experiences, Lack of Social Support or Modeling, Environmental and Structural Access Barriers, Cultural and Generational Barriers, Time & Life Demands. 

Our findings highlight the role of nature exposure in promoting reflective practices, identity development, and well-being in medical students. Students’ favorite outdoor memories emphasized the value of social connection, skill development, and personal growth. Reflections on barriers to the outdoors prompted students to consider both their own access to nature and that of future patients, inspiring creative thinking around the idea of “prescribing” nature in clinical care. A key limitation of this study is sample bias, as participants signed up for this nature-based course and may have held prior interest in or appreciation for the benefits of outdoor experiences. Future research is needed to determine the long-term effects of implementing nature as a reflective tool in medical education. 





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Non-small cell lung cancer (NSCLC), constituting about 85% of lung cancer cases, has a notably high incidence in Kentucky. Recent advancements have improved 5-year survival rates from a dismal 9.7% to approximately 18.4% with the introduction of combination chemotherapy and immune checkpoint inhibitors (ICIs) in 2018. Despite these gains, resistance to this combined therapy remains a significant issue. A proposed mechanism for such resistance involves CD73+ myeloid-derived suppressor cells (MDSCs), which may be induced by prostaglandin E2 (PGE2) accumulation following chemotherapy.
This study investigates the pathway through which platinum-based chemotherapy agents induce PGE2 overproduction, leading to the development of CD73+ MDSCs. We hypothesize that chemotherapy increases fatty acid synthase (FAS) activity, which elevates arachidonic acid (AA) levels. AA is a substrate for PGE2 synthesis, and its increased availability stimulates the NF-κB/CREB pathway, enhancing COX-2 activity and PGE2 production. The accumulated PGE2 is then thought to drive the formation of CD73+ MDSCs, which contribute to immunotherapy resistance by producing adenosine that suppresses T-cell activity.
Our findings, based on mass spectrometry, western blot analysis, and ELISA assays, demonstrate that chemotherapy significantly increases AA levels and activates the enzyme cascade responsible for PGE2 production. Mass spectrometry showed a doubling of AA concentration in chemotherapy-treated LLC cells compared to controls. Western blot analysis revealed enhanced FAS, NF-κB, and CREB activity in chemotherapy-treated KP cells. ELISA results indicated a marked increase in PGE2 levels in the supernatants of cisplatin-treated cells, even with PTGES knockdown, confirming the link between chemotherapy and elevated PGE2 levels.
Additionally, flow cytometry analysis in NSCLC mouse models revealed that chemotherapy treatment increased the percentage of CD73+ MDSCs from 10% to 20%. These results confirm that chemotherapy drives the overproduction of PGE2 and contributes to the expansion of CD73+ MDSCs, which may play a critical role in the resistance to immunotherapy. These insights could guide the development of targeted strategies to overcome resistance and improve therapeutic outcomes in NSCLC.

Chemotherapy induced accumulation of PGE2 & resulting immunotherapy resistance

Background
Chronic Kidney Disease of unknown etiology (CKDu) is a progressive form of kidney damage that disproportionately affects agricultural communities in Sri Lanka, Central America, and parts of India, with emerging hotspots also suspected in the South and Western United States. Despite extensive investigation, the underlying causes of CKDu remain unclear, partly because of a potential long lag between exposure and eventual symptomatic presentation of kidney disease. We applied machine learning (ML) and large language models (LLMs) to explore predictive tools for disease progression in a cohort of persons with CKDu, with the hypothesis that early identification of persons likely to experience kidney function decline will facilitate investigation of proximal (causative) exposures.
Methods
We analyzed baseline data (labs demographics) from 244 participants enrolled in Kidney Progression Project (KiPP), a prospective cohort of farmworkers with kidney disease in Sri Lanka launched in 2018. Among these participants, 22 progressed to the study’s primary outcome (eGFR<15 mL/min/1.73 m²) by the year 2024. For model development, we used data from the first 2.5 years of follow-up.
We developed and optimized several ML classifiers including logistic regression, support vector machines, random forests, and XGBoost. Each model was trained using stratified five-fold cross-validation, and class imbalance was addressed using SMOTE-based oversampling. To identify underlying patterns in the dataset, we applied unsupervised models like K-means clustering(k=7), and the resulting cluster labels were included as categorical features in supervised learning models. Model performance was evaluated using standard classification metrics, and interpretability was assessed using SHAP (SHapley Additive exPlanations). 
Additionally, we used clinical vignettes from KiPP data to evaluate the predictive capabilities of LLMs. We tested five LLMs through SecureGPT on their ability to predict progression.
Results
The best performing ML model was an XGBoost classifier (ROC-AUC= 0.84; F1 score= 0.48). SHAP analysis identified cluster membership and serum uric acid as key predictors. Among the LLMs, GPT 4.5 performed best (Accuracy= 87%; F1 score of 0.42). Both models outperformed existing methods (eGFR slope, Accuracy=67%, F1 score=0.23). 
Conclusion
Our findings demonstrate that both traditional ML models and LLMs can predict CKDu progression from limited clinical data. Custom-tailored ML models slightly outperformed generative AI models such as GPT-4.5. With continued refinement, these tools may not only support early identification of high risk individuals but also aid in identifying causes of CKDu. Future directions include external validation using ongoing international cohorts and incorporation of longitudinal laboratory data to generate clinically-deployable AI tools.


From Algorithms to Agriculture: Applying AI Methods to Predict Kidney Disease in Farmworkers

Background
Epileptic seizures disrupt normal brain activity, with pathological activity detectable on electroencephalograms. While seizure classification guides diagnosis and treatment, current systems often fail to capture individual variability in drug response, even among patients with similar clinical phenotypes. This dissertation applies dynamotype classification—a framework grounded in bifurcation theory and dynamical systems—to quantify seizure onset and offset patterns and their modulation by antiseizure medications (ASMs).
Methods
Data from over 7500 seizures was analyzed from 100+ mice from two models: the intra-amygdala kainate (IAK) model of temporal lobe epilepsy and the genetic Scn1a+/- model of Dravet syndrome. In the IAK model, spontaneous seizures were tracked over a 90-day period. Dynamotypes were visually scored in a randomized, blinded manner by two human raters and a custom DynamoSort machine learning tool we developed. Seizure onsets were categorized into three dynamotypes: (1) SupH, characterized by increasing spike amplitude (supercritical Hopf); (2) SNIC, by increasing frequency (saddle-node on invariant circle); and (3) SubH, by arbitrary or disorganized features. Seizure offsets were similarly categorized: SupH (decreasing amplitude), SNIC (decreasing frequency), and FLC (fold limit cycle, showing arbitrary declines).
Results
Longitudinal analysis of the IAK model revealed a temporal shift in dynamotype prevalence, with early seizures dominated by arbitrary transitions (SubH, FLC) and later seizures showing more structured bifurcation types (SupH, SNIC). Dynamotypes also predicted seizure duration and severity, with SNIC onset seizures being more severe and FLC offset seizures being longer in duration. Drug trials demonstrated that ASMs selectively modulated bifurcation types in a mechanism-dependent manner. GABAergic drugs (diazepam, phenobarbital) predominantly suppressed seizure onsets, indicating preferential disruption of initiation dynamics. In contrast, sodium channel blockers (phenytoin, valproate) exerted stronger effects on seizure terminations: phenytoin reduced SupH offsets, while valproate increased SNIC offsets in treatment responders, indicating SNIC offset seizures may be pharmacoresistant to VPA. Baseline dynamotype distributions also differed across models. Dravet mice displayed elevated SubH onset proportions relative to IAK, but GABAergic drugs caused similar dynamotype changes, showing conserved responses to ASMs across models.
Conclusion
Overall, this work demonstrates that bifurcation-informed seizure classification captures dynamic features of epileptogenesis and drug response, revealing mechanistically distinct signatures across ASM classes and models. These findings support the use of dynamotypes as a model-driven framework for evaluating treatment efficacy and tailoring therapies in epilepsy.

Seizure Dynamotype Patterns Predict Drug Response in Animal Models of Epilepsy

Oral contraceptive pills (OCPs) are one of the most widely used reversible contraceptive methods in the United States. Since their introduction in the 1960s, OCPs have been prescribed globally for birth control as well as management of hormonally influenced conditions including menstrual irregularities, menorrhagia, dysmenorrhea, acne, premenstrual syndrome, and hormonal migraines. Despite their therapeutic benefits, the effectiveness of OCPs varies between individuals, and the physiological mechanisms underlying these effects remain incompletely understood. While OCPs are known to influence menstrual bleeding patterns, little is known about their impact on the vasculature of the female reproductive system, specifically the uterine and ovarian arteries.
In comparison, intrauterine devices (IUDs) have been studied extensively for their vascular effects. Doppler ultrasound studies, which measure pulsatility index (PI) and resistance index (RI) as indicators of arterial blood flow, generally show no significant vascular changes pre- and post-IUD insertion. However, notable exceptions have been reported between copper and hormonal IUDs. Women experiencing abnormal bleeding with copper IUDs exhibited significant changes in PI and RI; however, another study demonstrated an increased RI in the uterine artery following levonorgestrel IUD (LNG-IUD) placement, potentially explaining its association with reduced menstrual bleeding. Given that copper IUDs tend to increase menstrual bleeding, while LNG-IUDs and OCPs typically reduce it, vascular changes may be an important underlying mechanism. This study aimed to investigate the effects of OCPs on uterine and ovarian artery blood flow using Doppler ultrasound in healthy, nulliparous women and to compare self-reported menstrual symptoms, including pain, cycle regularity, PMS manifestations, and skin changes using a standardized Menstrual Symptom Survey between OCP users and non-users.

The Vascular and Menstrual Symptom Impact of Oral Contraceptives: A Comparative Analysis of Uterine and Ovarian Artery Indices via Doppler Ultrasound

Background: Total hip arthroplasty (THA) alleviates pain and improves mobility in individuals with end-stage hip disease. However, THA patients with neuropathic bladder may face increased postoperative risks. This study aimed to assess how neuropathic bladder conditions influence outcomes after primary THA and propose recommendations to avoid associated complications.

Methods: A retrospective cohort analysis was conducted using data from TriNetX Research Network, including 193,988 patients aged 18 years and older who underwent primary THA between January 1, 2003, and January 1, 2020. Patients were categorized into two cohorts: those with (NB) and without (no-NB) neuropathic bladder conditions prior to THA. Propensity score matching yielded 1,482 patients in each cohort. Rates of postoperative outcomes at 30 days, 90 days, 1 year, and 5 years were compared.

Results: Within 30 days of surgery, NB patients demonstrated significantly elevated rates of urinary tract infection, pneumonia, periprosthetic joint infection (PJI), deep vein thrombosis, and transfusion compared to no-NB patients (P < 0.01). Similar trends continued at 90 days post-THA, with the NB cohort also showing a higher risk of pulmonary embolism and acute renal failure (P < 0.01). Furthermore, the NB cohort faced increased risks of PJI over a 1-year period (risk ratio (RR): 1.8; 95% confidence intervals (CI): 1.3, 2.4) and revision arthroplasty over a 5-year period (RR: 2.6; CI: 1.8, 3.8) compared to the no-NB cohort.

Conclusion: Patients with neuropathic bladder undergoing THA faced significantly elevated risks of postoperative complications, highlighting the need for tailored postoperative management strategies of neuropathic bladder to mitigate these risks and improve THA outcomes.

Assessing the Impact of Neuropathic Bladder on Total Hip Arthroplasty Outcomes:
A Matched Cohort Analysis

Evaluation of Cadaver Lung Tissue and the Histologic Characterization of Viral Interstitial Pneumonitis
Background
Histologic characterization offers a framework for the understanding and diagnosis
of viral interstitial pneumonitis (VIP). Moreover, due to the serious consequence in delayed diagnosis, in particular respiratory failure, the clinical findings and physiology of interstitial pneumonitis are fundamental to prevent mortality. Previous studies highlighted the high mortality associated with respiratory failure from VIP, emphasizing the urgency of diagnosis and treatment [1]. Thus, there is a need for earlier detection and treatment of VIP. Here, human cadaver lung tissue is evaluated and characterized in a cadaver with a known cause of death of “respiratory failure.” Notably, cadaveric studies are relevant due to pathological study of human remains being the gold standard for the diagnosis of many diseases.
Methods
Human lung tissue specimens were collected by dissection from a donated cadaver
made available by the department of anatomy. The samples were processed as standard
tissue specimens to create hematoxylin-eosin stained slides. A board-certified pathologist evaluated the slides under a light microscope to assess alveolar morphology, immune cells, and tissue architecture.
Results
The histologic slides were high quality without evidence of staining or other
artifacts from the embalming process. The lung sections showed diffuse thickening of the alveolar septa with some cuboidalization of the alveolar epithelium. Furthermore, there was presence of a predominantly lymphocytic infiltrate within the lung interstitium, in the absence of bacteria. Together, these findings are highly suggestive of viral interstitial pneumonitis as the underlying mechanism of death.
Conclusion
Lung tissue from human cadavers is particularly useful to observe the histologic
manifestations of lung disease. This cadaveric study and microscopic findings highlight the feasibility in histologic analysis to diagnose viral interstitial pneumonitis. In contrast to the neutrophil-predominant intra-alveolar exudate seen in pneumonia, VIP presents with lymphocyte-predominant interstitial inflammatory infiltrate. By leveraging lung samples and histological analysis at an earlier time, physicians can more rapidly diagnose VIP and initiate appropriate therapy. This ultimately will reduce the risk of respiratory failure and prevent mortality.


Evaluation of Cadaver Lung Tissue and Histologic Characterization of Viral Interstitial Pneumonitis

Abstract Title
Propofol-Induced Cannabinoid Hyperemesis Syndrome in a Chronic Cannabis User

Background

Cannabinoid Hyperemesis Syndrome (CHS) is a paradoxical condition in chronic cannabis users characterized by cyclic nausea and vomiting, despite cannabis's known antiemetic effects. Propofol-induced Cannabinoid Hyperemesis Syndrome (PI-CHS) is an emerging, under-recognized phenomenon in which propofol anesthesia may trigger CHS-like symptoms in chronic cannabis users. The suspected mechanism involves propofol’s lipophilic nature mobilizing stored delta-9-tetrahydrocannabinol (THC) from adipose tissue, potentially precipitating an acute emetic response.

Case Presentation

A 56-year-old male presented at the stand-alone surgical center for a routine screening colonoscopy. He reported daily cannabis use for over 40 years, with no history of chronic illness, alcohol, or tobacco use. His previous colonoscopy, performed six years prior, was followed by severe nausea and vomiting, which he attributed to an unknown cause at the time. Preoperative evaluation was otherwise unremarkable. He denied baseline gastrointestinal symptoms, cyclic vomiting, or any personal or family history of anesthesia complications. Following the current colonoscopy, which was performed under propofol sedation, the patient again developed intractable nausea and vomiting within hours post-procedure, necessitating emergency department evaluation. Workup revealed no surgical or infectious cause. Supportive care and cessation of cannabis use led to resolution of symptoms over several days.

Discussion

This case highlights a potential association between propofol administration and the unmasking of CHS in chronic cannabis users. The theory that propofol may mobilize THC from fat stores into circulation offers a possible mechanism for PI-CHS. As cannabis use continues to rise, anesthesiologists and perioperative providers should be aware of this phenomenon. Preoperative screening for chronic cannabis use may help identify at-risk patients and guide perioperative planning. Further studies are needed to elucidate the pathophysiology of PI-CHS and to establish appropriate prevention and management strategies.


Propofol-Induced Cannabinoid Hyperemesis Syndrome in a Chronic Cannabis User: A Case Report

Title- Intravascular Lithotripsy as a Facilitator for Stenting in Heavily Calcified Subclavian Artery Lesions: A Clinical Case Report

Background- Subclavian artery stenosis is a rare but clinically significant cause of
vertebrobasilar insufficiency and upper extremity ischemia. Management is challenging, especially in the presence of heavily calcified lesions. Intravascular lithotripsy (IVL) is an emerging adjunctive therapy for calcified lesions, primarily studied in coronary and lower extremity vessels; however, its application in subclavian interventions remains minimally studied.

Case Presentation- A 78-year-old female with a history of hypertension, diabetes,
Hyperlipidemia and peripheral artery disease presented with recurrent dizziness and nausea. Carotid duplex demonstrated moderate left internal carotid stenosis and retrograde flow in the left vertebral artery, consistent with subclavian steal syndrome. Subsequent angiography revealed 90% eccentric, calcified stenosis of the proximal left subclavian artery. Given the lesion’s heavy calcification, the patient underwent percutaneous intervention with a Serranator scoring balloon, followed by intravascular lithotripsy (IVL) using an 8x30 mm peripheral balloon (2 rounds, 30 pulses each). This resulted in significant lesion modification and relief of the balloon waist. A 7x27 mm Express LD bare-metal stent was then deployed with excellent angiographic results and residual stenosis <10%. IVUS confirmed adequate stent expansion. The procedure was performed via right femoral access with no complications. Post-procedure, the patient reported complete resolution of dizziness and no further symptoms of vertebrobasilar insufficiency. She was discharged on dual antiplatelet therapy for 30 days, followed by aspirin monotherapy. At 1-month follow-up, she remained asymptomatic with a patent stent and no evidence of restenosis.

Discussion- This case highlights the utility of IVL in treating severely calcified subclavian artery stenosis. Traditional balloon angioplasty and stenting in such lesions are associated with suboptimal expansion, risk of dissection, and restenosis. IVL offers a safe and effective means of calcium modification, facilitating optimal stent deployment. To our knowledge, reports of IVL use in subclavian arteries are scarce, and this case adds to the growing body of evidence supporting its application in complex peripheral vascular interventions. Intravascular lithotripsy is a valuable adjunct in the endovascular management of heavily calcified subclavian artery stenosis, enabling successful stent placement and symptomatic relief. Further studies are warranted to define its role in this vascular territory.

Intravascular Lithotripsy as a Facilitator for Stenting in Heavily Calcified
Subclavian Artery Lesions: A Clinical Case Report

Background: Both language concordance and Medicaid acceptance are essential for equitable surgical care. Spanish-speaking and other multilingual surgeons improve comprehension, satisfaction, and outcomes for patients with limited English proficiency (LEP), while Medicaid participation ensures economic accessibility. Chicago houses more than 1.7 million residents who speak a language other than English—predominantly Spanish—and over 25 % of city adults rely on Medicaid. Whether large academic health systems have enough Medicaid-accepting, Spanish-
speaking, or otherwise multilingual surgeons to meet this dual need remains unclear. The objective was to determine if Medicaid-accepting Spanish-speaking or multilingual surgical specialists are disproportionately underrepresented at the five largest academic institutions in Chicagoland.

Methods: In February–July 2025, publicly available roster data were extracted from surgeons in general surgery, orthopedics, neurosurgery, urology, vascular surgery, otolaryngology, ophthalmology, OB/GYN, and dermatology from Rush, Northwestern, the University of Chicago, Loyola, and UIC. Variables collected included languages spoken, accepted insurance plans, and clinical practice ZIP codes. Surgeons were deemed multilingual if ≥2 languages were listed and Spanish- speaking if Spanish was explicitly reported. Medicaid acceptance was coded when any Illinois Medicaid plan (e.g., CountyCare, Meridian, Molina, MMAI) appeared among accepted insurances. Descriptive statistics compared proportions of (1) multilingual, (2) Spanish-speaking, (3) Medicaid-accepting, and (4) Medicaid accepting + Spanish-speaking surgeons across institutions and specialties.

Results: Among 1087 surgical specialists identified, 204 (18.8%) were multilingual, and 96 (8.8%) spoke Spanish. Medicaid was accepted by 278 providers (25.6%); however, only 38 surgeons (3.5%) met the combined criteria of Medicaid-accepting & Spanish-speaking, and 66 (6.1%) were Medicaid-accepting & multilingual. Representation varied markedly by institution: Rush (5.5 %) and UIC (6.3 %) had the highest share of Medicaid-accepting Spanish-speaking surgeons, whereas Northwestern and the University of Chicago had <3 %. Specialty analysis showed the lowest dual-access representation in neurosurgery (1.8 %) and vascular surgery (2.4 %). Geographic mapping revealed that many Medicaid-accepting multilingual surgeons practiced in affluent downtown ZIP codes, leaving LEP-dense neighborhoods with few or no language-concordant, Medicaid-accessible surgical specialists.

Conclusion: Medicaid-accepting Spanish-speaking and multilingual surgeons are indeed under-represented within Chicago’s largest academic institutions and are unevenly distributed away from high-need LEP communities. Addressing this dual disparity will require institution-specific recruitment incentives, multilingual training pipelines, and strategic placement of surgical services in linguistically diverse, Medicaid-reliant neighborhoods to close persistent gaps in access and outcomes.

Language and Medicaid Access Disparities Among Surgical Specialists at Major Chicagoland Institutions

Lung cancer is a silent killer, often going undetected until it’s too late. Early diagnosis greatly improves outcomes—stage I patients have a 97% two-year survival rate, compared to just 23% at stage IV. However, most diagnoses occur at later stages due to limitations in current screening methods. Current detection methods, namely Gas chromatography–mass spectrometry (GC-MS) and CT scans, are costly or invasive, leading to most diagnoses at advanced stages. These methods require in-lab operation, and are inaccessible to rural or resource-limited settings. While several studies have explored small-scale sensor systems as an alternative, they often suffer from reduced accuracy, high production costs, and long processing times—sometimes exceeding 40 minutes per breath sample. Therefore, there is a need for a fast, affordable, and accurate breath-based lung cancer detection system suitable for those with limited resources. To address this, we developed an affordable breath-based lung cancer screening device featuring a gas chamber and machine learning model. It analyzes VOC composition in human breath, influenced by lipid peroxidation in cancer cells, to compare profiles between healthy and lung cancer samples. The system was designed to detect more than 30 volatile organic compounds (VOCs) using twelve metal oxide semiconductor sensors and one chemi-resistive alkane sensor. In total, 46 clinical breath samples were collected (from 28 healthy controls and 18 lung cancer patients) in 1L Tedlar bags. Electrical resistance data from the sensor array was processed using a multilayer perceptron (MLP) neural network. The model architecture incorporated dropout and batch normalization layers to minimize overfitting. Sensor output patterns were statistically analyzed using the Mann-Whitney U test, which confirmed significant differences between cancer and control groups (p < 0.05). Model performance was evaluated using five-fold cross-validation to ensure robustness and generalizability. The final model achieved an accuracy of 96.57%, sensitivity of 92.88%, and specificity of 98.82%, with an area under the ROC curve (AUC) of 0.9913. Low standard deviation across folds indicated consistent classification performance. The complete system was able to produce diagnostic results in about 10 minutes per breath sample. Our system outperforms existing methods of lung cancer detection through MOS sensors by greater than 5% while costing only around $300. These findings suggest that the breath analyzer system proposed in this study has the potential for preliminary lung cancer screening. 





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Chemotherapy induced accumulation of PGE2 & resulting immunotherapy resistance

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