Risk Categories Research Articles

Implementing standard practices in the safe handling of gene therapy and biohazardous drugs in a health-system setting.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. This publication outlines development of the infrastructure and standard operating procedures (SOPs) for handling of gene therapy drugs within an integrated health-system setting. These guidelines aim to fill the gap in occupational safety standardization, as current materials focus on precautions in a research laboratory setting and do not fully take into account occupational hazards for pharmacists, technicians, and other medical staff. Pharmacists in a large integrated healthcare system recognized the gap in knowledge as well as lack of standard procedures in handling gene therapy drugs in the pharmacy setting and sought to establish updated best practices. The objectives were to implement the necessary infrastructure and SOPs for the handling, compounding, and cleanup of gene therapy drugs and to update existing resources such as spill kits to reflect the new SOPs. Critical milestones included establishing a new biohazardous drug risk classification and standardizing medication labeling. These steps were necessary to ensure consistent and safe handling across the enterprise. With the increasing prevalence of gene therapy drugs, it is of paramount importance to establish best practices to ensure occupational safety. While existing regulations and literature outline basic handling guidelines for laboratory use, there is a limited amount of information in relation to pharmacy departments within healthcare groups. In establishing robust SOPs surrounding the handling, compounding, and management of gene therapy drugs, pharmacy groups can better ensure both patient and staff safety.

The impact of individual comorbidities in transplant recipients receiving post-transplant cyclophosphamide.

Post-transplant cyclophosphamide (PTCY) is increasingly used as effective graft-versus-host disease (GvHD) prophylaxis in allogeneic hematopoietic-cell transplantation (allo-HCT). However, PTCY is associated with toxicities. Whether patients with specific comorbidities are more vulnerable to cyclophosphamide-induced toxicity is unclear. We retrospectively evaluated the impact of individual organ dysfunctions for non-relapse mortality (NRM) risk and overall survival (OS) among 5888 adults who underwent PTCY-based allo-HCT for acute myeloid leukemia between 2010 and 2023. In multivariable analyses 5 of the comorbidities (renal, moderate/severe hepatic, cardiac including arrhythmia/valvular disease, severe pulmonary, infection) were independently associated with adverse NRM and OS without influencing relapse rate. A simplified model using the absence (n = 4390), presence of 1 (n = 1229) or presence of 2 or 3 (n = 269) of the comorbidities which were determined individually to contribute to NRM stratified patients into 3 NRM risk (16.2% vs. 21.6% vs. 36%, retrospectively) and OS categories (64% vs. 56% vs. 36.4%, retrospectively). In Cox model, recipients with 2 or 3 comorbidities had an increased hazard ratio for NRM of 2.38 (95% confidence interval [CI], 1.89-3) and for OS of 1.96 (95% CI 1.64-2.33). Whether patients with concomitant diagnoses, as determined here, may benefit from a reduced PTCY dose remains to be evaluated in prospective clinical trials.

Cataract surgery risk stratification models: a systematic review.

Risk stratification models can assist cataract surgeons in clinical decision-making by categorizing patients into distinct groups based on their likelihood of complications. In this systematic review, we assess the characteristics of cataract surgery risk stratification models. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines, we searched six databases (PubMed, OVID, Embase, CINAHL, Cochrane Trials, and Web of Science) in January 2024. We included peer-reviewed, full-text, English-language studies describing models used preoperatively to assess the likelihood of complications in cataract surgery. We excluded letters, editorials, and non-peer-reviewed publications, such as conference abstracts and studies describing predictive models that did not group the patients into distinct risk categories. We constructed a checklist from three frameworks to critically appraise the participants, predictors, and risk of bias in the models. Of 4192 articles, eight met the inclusion criteria. Most models were designed for attending surgeons only and for phacoemulsification to predict zonular complications and posterior capsule rupture. The most common risk factors identified in the models were poor patient positioning, advanced age, small pupils, and pseudoexfoliation syndrome. Methodological limitations included the lack of multivariable modeling, standardized outcome measures, and external validation. Cataract surgeons should understand the limitations of cataract surgery risk stratification models. Existing models can be improved with more robust methods, the use of standardized metrics, and external validation.

Kajian Perilaku Petani Bawang Daun (Allium Fistulosum L.) dalam Mereduksi Risiko sebagai Upaya Meningkatkan Produktivitas Usahatani di Desa Linonggasay

The purpose of this study was to determine the amount of income and the amount of risk of Scallion farming and to describe the behavior of farmers in reducing the risk of onion farming in Konawe Regency. The study was conducted in 2023 at the onion production center in Linonggasay Village, West Wonggeduku District with a total sample of 20 farmers. The analysis used was income analysis and descriptive analysis of the risk level using the variation coefficient (%). The results showed that the average net income was IDR 12,788,000 per planting season. Scallion farming is included in the category of farming with a risk of less than 0.5, namely the use of equipment (0.05), the use of pesticides (0.13), and the use of labor in fertilization (0.32), while the rest have moderate risks, namely harvest labor (0.51), planting labor (0.59), seeds (0.64), fertilizer (0.72), and processing (0.90) and none in the high risk category. Some of the farmer behaviors carried out to reduce risk include: buying seeds from saprodi kiosks, controlling plant pests using chemical fertilizers, using recommended planting distances, utilizing labor from outside the village, and in the event of failure, taking savings to meet family needs.

Testicular Germ Cell Tumors: A Review.

Testicular cancer is the most common solid malignancy among males aged 15 to 40 years in the US, with approximately 10 000 new cases diagnosed each year. Between 90% and 95% of testicular cancers are germ cell tumors (GCTs). The mean age at diagnosis for testicular cancer is 33 years. GCTs are categorized as seminomas and nonseminomatous GCTs (NSGCTs) based on their embryonic origins and path of differentiation. Risk factors include cryptorchidism, family history of testicular cancer, gonadal dysgenesis, infertility, cannabis use, and genetic conditions such as Klinefelter syndrome. The most common presenting symptom of testicular cancer is a painless testicular mass. History, physical examination, scrotal ultrasound, laboratory assessment of GCT-associated serum tumor markers (α-fetoprotein, β-human chorionic gonadotropin, and lactate dehydrogenase), and prompt referral to a urologist are indicated when testicular cancer is suspected. Early diagnosis and treatment, starting with a radical inguinal orchiectomy, are important to optimize outcomes. At diagnosis, GCT is stage I (localized to the testicle) in 70% to 75% of patients, stage II (metastatic only to the retroperitoneal lymph nodes) in 20%, and stage III (widely metastatic) in 10%. Treatment of GCTs is guided by histology, clinical staging, and risk classification, with 5-year survival rates of 99%, 92%, and 85% for those diagnosed at stages I, II, and III, respectively. Optimal treatment often involves a multidisciplinary team at high-volume, experienced medical centers and may include surveillance (serum tumor markers [α-fetoprotein, β-human chorionic gonadotropin, and lactate dehydrogenase] and imaging of the chest, abdomen, and pelvis), surgery (retroperitoneal lymph node dissection), chemotherapy, and/or radiation. Treatment decisions should consider long-term survivorship concerns, including body image, fertility, hypogonadism, mental health, financial cost, adherence to follow-up, and late adverse effects of therapy such as cardiovascular disease, secondary malignancies, and potential psychosocial effects such as anxiety, depression, and social isolation. Testicular cancer is the most common solid malignancy in young men in the US, and 90% to 95% are GCTs. Patients with testicular GCT have a 5-year survival rate of 99%, 92%, and 85% for stages I, II, and III, respectively. Prompt diagnosis and treatment are important to optimize outcomes, and treatment decisions should balance oncologic control with survivorship concerns to minimize long-term adverse effects of treatment.

The Present State and Potential Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.

An aberrant increase in cancer incidences has demanded extreme attention globally despite advancements in diagnostic and management strategies. The high mortality rate is concerning, and tumour heterogeneity at the genetic, phenotypic, and pathological levels exacerbates the problem. In this context, lack of early diagnostic techniques and therapeutic resistance to drugs, sole awareness among the public, coupled with the unavailability of these modern technologies in developing and low-income countries, negatively impact cancer management. One of the prime necessities of the world today is the enhancement of early detection of cancers. Several independent studies have shown that screening individuals for cancer can improve patient survival but are bogged down by risk classification and major problems in patient selection. Artificial intelligence (AI) has significantly advanced the field of oncology, addressing various medical challenges, particularly in cancer management. Leveraging extensive medical datasets and innovative computational technologies, AI, especially through deep learning (DL), has found applications across multiple facets of oncology research. These applications range from early cancer detection, diagnosis, classification, and grading, molecular characterization of tumours, prediction of patient outcomes and treatment responses, personalized treatment, and novel anti-cancer drug discovery. Over the past decade, AI/ML has emerged as a valuable tool in cancer prognosis, risk assessment, and treatment selection for cancer patients. Several patents have been and are being filed and granted. Some of those inventions were explored and are being explored in clinical settings as well. In this review, we will discuss the current status, recent advancements, clinical trials, challenges, and opportunities associated with AI/ML applications in cancer detection and management. We are optimistic about the potential of AI/ML in improving outcomes for cancer and the need for further research and development in this field.

Nailfold capillary abnormalities as indicators of diabetic nephropathy progression: a cross-sectional study in type 2 diabetes

Background Type 2 diabetes mellitus (DM) leads to chronic hyperglycemia and microvascular complications, including diabetic nephropathy (DN). Nailfold videocapillaroscopy (NVC) is a non-invasive method for assessing the microvascular abnormalities and may aid in early DN detection. This study investigates the correlation between DN and nailfold capillary abnormalities in type 2 DM. Methods This prospective cross-sectional study involved 453 participants with type 2 DM, divided into 3 groups based on estimated glomerular filtration rate (eGFR) or albumin-to-creatinine (A/C) ratio. Chronic kidney disease (CKD) risk categories based on the 2012 Kidney Disease Improving Global Outcomes (KDIGO) guidelines. Participants underwent structured interviews, clinical assessments, and laboratory tests. NVC was performed to assess capillary morphology, distribution, density, and blood flow. Results Significant differences in NVC measurements were observed across eGFR groups, with higher NVC scores and more severe capillary abnormalities noted in lower eGFR groups. The ‘comma-like’ capillary distribution pattern (23.08%, p = 0.016), more ratio of ramified capillaries (p = 0.0137) were observed in the eGFR <30 ml/min/1.73m2 group. Reduced fundus transparency (p = 0.0015) and impaired visibility of the sub-venous plexus (p = 0.0016) were noted in the lower eGFR groups. Capillary lengths, both input and mean, were shorter in the A/C ratio > 300 mg/g group (p = 0.0382 and p = 0.0478, respectively). The NVC score was higher in the A/C ratio > 300 mg/g groups (p = 0.0028). Conclusion Nailfold capillary abnormalities correlate with DN severity, indicating their potential as a non-invasive biomarker for early DN detection and monitoring in type 2 DM.

Therapeutic efficacy of immune-oncology combination therapy in advanced renal cell carcinoma without prior nephrectomy.

Immuno-oncology (IO) combination therapies, including IO + IO or IO + vascular endothelial growth factor targeted therapies (VEGF-TT), have become the standard first-line treatment for advanced renal cell carcinoma (RCC). However, the optimal regimen for patients without prior nephrectomy remains unclear. Data from 99 patients with advanced RCC without nephrectomy, treated with VEGF-TT, IO + IO, or IO + VEGF-TT between May 2008 and May 2024, were retrospectively reviewed and analyzed. Patients were divided into VEGE-TT, IO + IO, and IO + VEGF-TT groups based on their first-line treatment, and survival and tumor response were compared. All patients included in this study were categorized as either intermediate or poor risk according to the International Metastatic RCC Database Consortium risk classification. Among the 99 included patients, 41 initiated first-line therapy with VEGF-TT, 36 with IO + IO, and 22 with IO + VEGF-TT. The objective response rates were 17.5% for VEGF-TT, 38.9% for IO + IO, and 61.9% for IO + VEGF-TT. Notably, the IO + VEGF-TT group showed the greatest shrinkage of target kidney lesions (p = 0.0042). In multivariate analyses, bone metastasis (hazard ratio (HR) = 1.812, 95% confidence interval (CI) 1.017-3.228, p = 0.0436) and the first-line regimen (VEGF-TT vs IO + VEGF-TT: HR = 0.129, 95% CI 0.045-0.369, p = 0.0001) were independent prognostic factors for progression-free survival. The first-line regimen (VEGF-TT vs IO + VEGF-TT: HR = 0.303, 95% CI 0.104-0.879, p = 0.0279) independently affected overall survival. IO combination therapy, especially IO + VEGF-TT, has demonstrated a higher anti-tumor response in patients with advanced RCC without nephrectomy and may also be highly effective against primary renal tumors. Therefore, further studies are needed to improve patient survival and validate efficacy of IO combination therapy.

Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases

Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human, financial, and clinical resources in healthcare systems worldwide. This cohort study evaluated three machine learning algorithms—XGBoost, Elastic Net logistic regression, and an Artificial Neural Network—to develop a prediction model for three outcomes: mortality, hospitalization, and emergency department visits. The objective was to build a clinical decision support system for patients with noncommunicable diseases treated at the Alma Mater Hospital complex in Medellín, Colombia. We collected 4845 electronic medical record entries from 5000 patients included in the study. The median age was 71.83 years, with 63.8% women and 29.7% receiving home care. The most prevalent medical conditions were diabetes (52.9%), hypertension (67.2%), dyslipidemia (57.3%), and COPD (19.4%). For mortality prediction, the Elastic Net logistic regression model achieved an AUCROC of 0.883 (95% CI: 0.848–0.917), the XGBoost model reached an AUCROC of 0.896 (95% CI: 0.865–0.927), and the Neural Network achieved 0.886 (95% CI: 0.853–0.916). For hospitalization, the Elastic Net model had an AUCROC of 0.952 (95% CI: 0.937–0.965), the XGBoost model achieved 0.963 (95% CI: 0.952–0.974), and the Neural Network scored 0.932 (95% CI: 0.915–0.948). For emergency department visits, the AUCROC values were 0.980 (95% CI: 0.971–0.987) for Elastic Net, 0.977 (95% CI: 0.967–0.986) for XGBoost, and 0.976 (95% CI: 0.968–0.982) for the neural network. A dashboard was developed to interact with an ensemble risk categorization segmenting patient risk in the cohort to aid in clinical decision-making. A clinical decision support system based on artificial intelligence using electronic medical records possibly can help segmenting the risk in populations with Noncommunicable Diseases for effective decision-making.

Trends in depressive symptoms among high school students with and without health-risk behaviors in the United States: Apopulation-based study.

Despite the recent rise in depressive symptoms among adolescents, trends in at-risk groups remain poorly understood. This study estimated national trends in depressive symptoms among high school students with and without health-risk behaviors. Data were analyzed from the 2007 to 2021 Youth Risk Behavior Survey (YRBS), a biennial cross-sectional survey of US high school students (n=119,654). Depressive symptoms were defined as past-year feelings of sadness/hopelessness almost every day for ≥2 weeks impacting usual activities. 24 health-risk behaviors across five categories (violence, substance use, sexual, physical/sedentary, and weight-related) were considered. Multivariable-adjusted logistic regression identified health-risk behaviors independently associated with depressive symptoms. National trends in depressive symptoms among students with and without these health-risk behaviors were estimated. National trends in health-risk behaviors were also estimated among those with depressive symptoms. Among 119,654 students (mean age, 16.0 [SD=1.2] years; 50.1% females; and 45.3% non-Hispanic White), depressive symptoms increased from 28.4% (2007) to 42.3% (2021) (biennial average percent change (BAPC), 3.0% [95% confidence interval (CI), 2.6-3.4%]). The largest increases occurred among females (BAPC, 3.3% [2.9-3.8%]) and non-Hispanic White students (BAPC, 3.6% [3.0-4.1%]). Depressive symptoms increased more among students engaging in ≤1 risk category (BAPC=4.6% [3.6-5.7%]) than students engaging in 4-5 risk categories (BAPC=2.9% [2.4-3.3%]) (interaction, p=0.037) and there was a general trend toward decreasing engagement in risk behaviors among students with depressive symptoms. In subgroup analyses, there was a disproportionately large increase in depressive symptoms among students reporting cannabis use, high-risk screen time, shorter sleep hours, and unhealthy weight perception. Depressive symptoms increased faster among high school students without than with multiple behavioral risk factors. A broadening of the depression risk profile underscores the potential value of universal depression screening to identify students who may otherwise go undetected by targeted screening. The study was not funded.

Tackling ALT-positive neuroblastoma: is it time to redefine risk classification systems? A systematic review with IPD meta-analysis.

The heterogeneous prognosis in neuroblastoma, shaped by telomere maintenance mechanisms (TMMs), notably the alternative lengthening of telomeres (ALT) pathway, necessitates a refined risk classification for high-risk patients. Current systems often lack precision, hindering tailored treatment approaches. This individual participant data (IPD) meta-analysis of survival among ALT-positive patients aims to improve risk classification systems, enhancing therapeutic strategies and patient outcomes. Following PRISMA-IPD guidelines, we conducted a comprehensive review of neuroblastoma patients retrieved from PubMed, Scopus, and Embase databases until March-2024. Patients were stratified into ALT-positive and TMM-negative subgroups. Overall and event-free survival probabilities were evaluated. In our cohort of 293 patients (156 ALT-positive, 137 TMM-negative) obtained from eight different studies, ALT-positive individuals displayed lower survival rates than TMM-negative patients. Non-stage 4 ALT-positive patients had reduced overall and event-free survival probabilities compared to their TMM-negative counterparts, indicating potential misclassification. Stage 4 ALT-positive patients similarly showed poorer survival outcomes than non-stage 4 TMM-negative patients, underscoring the significance of ALT in patient prognosis. Our study highlights poorer outcomes in ALT-positive neuroblastoma patients, emphasizing the need to integrate TMM status into international risk classification guidelines. Standardizing TMM assessment is key for refining treatment strategies, considering the unique biology of ALT-positive patients.

Risk Categorization of Foods of Non-Animal Origin Subject to United Kingdom Import Controls.

Significant volumes of foods are traded between countries and this trade brings with it opportunities for foodborne hazards to cross borders and pose a public health risk. The Competent Authority in the importing country has a duty to protect its population from these foodborne hazards. Due to the high volumes of imported food as well as World Trade Organization requirements, border checks need to be prioritized in a risk-based manner. This represents an enormous practical challenge to which efficient solutions are required. This paper describes an approach to risk categorization and ranking of imported Foods of Non-Animal Origin on a food/country of origin/hazard (combination) basis. Combinations were scored using data on product characteristics, hazard characteristics and import volumes, with weightings applied following external consultation. The described approach differs from most others in that it categorizes and ranks microbiological, mycotoxin and chemical hazards with a single approach and on the same scale, allowing for meaningful comparison between the combinations. The output is a risk categorization, a relative risk ranking and a heat map, all of which provide information that can be used alongside other data by risk managers when prioritizing interventions at the border.

Fully automated coronary artery calcium score and risk categorization from chest CT using deep learning and multiorgan segmentation: A validation study from National Lung Screening Trial (NLST).

The National Lung Screening Trial (NLST) has shown that screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. These patients are also at high risk of coronary artery disease, and we used deep learning model to automatically detect, quantify and perform risk categorisation of coronary artery calcification score (CACS) from non-ECG gated Chest CT scans. Automated calcium quantification was performed using a neural network based on Mask regions with convolutional neural networks (R-CNN) for multiorgan segmentation. Manual evaluation of calcium was carried out using proprietary software. This study used 80 patients to train the segmentation model and randomly selected 1442 patients were used for the validation of the algorithm. We compared the model generated results with Ground Truth. Automatic cardiac and aortic segmentation model worked well (Mean Dice score: 0.91). Cohen's kappa coefficient between the reference actual and the interclass computed predictive categories on the test set is 0.72 (95% CI: 0.61-0.83). Our method correctly classifies the risk group in 78.8% of the cases and classifies the subjects in the same group. F-score is measured as 0.78; 0.71; 0.81; 0.82; 0.92 in calcium score categories 0(CS:0), I (1-99), II (100-400), III (400-1000), IV (>1000), respectively. 79% of the predictive scores lie in the same categories, 20% of the predictive scores are one category up or down, and only 1.2% patients were more than one category off. For the presence/absence of coronary artery calcifications, our deep learning model achieved a sensitivity of 90% and a specificity of 94%. Fully automated model shows good correlation compared with reference standards. Automating the process could improve diagnostic ability, risk categorization, facilitate primary prevention intervention, improve morbidity and mortality, and decrease healthcare costs.

Impact of Funding Source on Long-Term Outcomes in Prostate Cancer: Analysis of a Large Public Database From Sao Paulo, Brazil

PURPOSE We investigated the impact of the funding source (public v private) on the overall survival (OS) of men with prostate cancer in Brazil. METHODS We retrospectively analyzed patients with prostate cancer from a large hospital registry from the state of São Paulo, Brazil. Patients age 50-99 years diagnosed with prostate acinar adenocarcinoma or adenocarcinoma not otherwise specified (NOS) between January 2014 and December 2017 were eligible. Demographic and clinical features were analyzed alongside the funding source. On the basis of clinical characteristics at diagnosis (lymph node status, distant metastasis, prostate-specific antigen [PSA], and Gleason score), patients were categorized into low-risk, intermediate-risk, high-risk, and metastatic groups. RESULTS Of 25,009 patients analyzed, 85% had a public funding source. These patients were slightly older, had greater proportion of adenocarcinoma NOS, and higher PSA levels and risk category. There was a significant difference in OS favoring patients with a private funding source ( P &lt; .0001). The estimated OS rates at 5 years were 76.2% (95% CI, 75.6 to 76.9) and 86.9% (95% CI, 85 to 88.7) for the public and private groups, respectively ( P &lt; .0001). The funding source was significantly associated with OS independent of age, educational level, and receipt of any treatment in the intermediate-risk (hazard ratio [HR], 2.28 [95% CI, 1.58 to 3.30]; P &lt; .001) and high-risk (HR, 1.36 [95% CI, 1.02 to 1.80]; P = .04) groups, but not in the low-risk (HR, 0.85 [95% CI, 0.60 to 1.21]; P = .38) or metastatic groups (HR, 0.84 [95% CI, 0.64 to 1.11]; P = .23). CONCLUSION The worse OS observed for patients with prostate cancer with a public source of funding underscores the need for actions directed to improve the standards of public health care in Brazil.

Prognostic models for progression-free survival in atypical meningioma: Comparison of machine learning-based approach and the COX model in an Asian multicenter study.

Atypical meningiomas are prevalent intracranial tumors with varied prognoses and recurrence rates. The role of adjuvant radiotherapy (ART) in atypical meningiomas remains debated. This study aimed to develop and validate a prognostic model incorporating machine learning techniques and clinical factors to predict progression-free survival (PFS) in patients with atypical meningiomas and assess the impact of ART. A retrospective review of 669 patients from five institutions in Korea and China was conducted. Cox proportional hazards, gradient boosting machine, and random survival forest models were employed for comparative analysis, utilizing both internal and external validation sets. Model performance was assessed using Harrell's concordance index and permutation feature importance. Of 581 eligible patients, age, post-operative platelet count, performance status, Simpson grade, and ART were identified as significant prognostic factors across all models. In the ART subgroup, age and tumor size were the top prognostic indicators. The Cox model outperformed other methods, achieving a training C-index of 0.73 (95 % CI: 0.72-0.73) and an external validation C-index of 0.74 (95 % CI: 0.73-0.74). The model effectively stratified patients into risk categories, revealing a differential impact of ART: low-risk patients in the active surveillance group showed a 5.6 % improvement in 5-year PFS with predicted ART addition, compared to a 15.9 % improvement in the high-risk group. This multicenter study offers a validated prognostic model for atypical meningiomas, highlighting the need for tailored treatment plans. The model's ability to stratify patients into risk categories for PFS provides a valuable tool for clinical decision-making, potentially optimizing patient outcomes.

Identifying key risks to the stability of Ukrainian universities during wartime

Ukrainian universities operate under uncertain conditions and numerous challenges during wartime, significantly affecting their stability. This paper aims to identify and classify the key risks to the stability of Ukrainian universities during wartime and propose proactive tools for their mitigation. The study focuses on financial, personnel, and social risks, which are highlighted as the most critical under current circumstances. The data were sourced from the official websites of 10 universities, Open Budget, and the official websites of the Ministry of Finance of Ukraine and the State Statistics Service of Ukraine. The methodology combines theoretical analysis and statistical evaluation, including calculating marginal income – representing the portion of income covered by variable costs – and the operating margin ratio, defined as the ratio of marginal income to total income. Financial risks include a 7.9% reduction in education budget allocations, decreased subsidies, and insufficient state support. Personnel risks are analyzed through indicators, e.g., uncompetitive average hourly wages, leading to staff attrition and reduced motivation. The findings propose a multi-level classification of risks, categorizing them into external and internal with subcategories such as financial, personnel, and social ones. Non-traditional financial measures, such as unpaid leave and allowance reductions, were observed as short-term crisis strategies but require further evaluation to assess their long-term impact. The paper contributes to academic discourse by outlining challenges to university stability during wartime and providing a foundation for future research into effective risk mitigation strategies.

Social determinants of health and financial burden among patients with pancreatic cancer at a tertiary cancer center.

678 Background: Financial toxicity (FT) represents a challenge for cancer patients that impacts treatment decisions, patient satisfaction, and quality of life. Social determinants of health (SDoH) screening may predict who is at risk of FT and could potentially benefit from financial interventions. Methods: This is a retrospective study of all cancer patients presenting to Mayo Clinic sites between Sept 2023 – Jun 2024. Patients completed SDoH questionnaires and were given risk scores of Low Risk, Medium Risk, and High Risk for SDoH categories: Financial Strain Risk, Transportation Risk, and Food Insecurity Risk. Patient financial data including active debt amounts, insurance coverage, and financial aid were collected. Patients with pancreatic ductal adenocarcinoma (PDAC) were compared to those with pancreatic neuroendocrine tumors (PNET) and patients with other cancers (Others). Categorical variables were compared with Pearson's Chi-squared test, continuous variables with Kruskal-Wallis rank sum test. P values &lt; 0.05 were considered significant. Results: Data was obtained from 88,039 patients, 2,607 of whom had PDAC and 449 had PNET. Compared to PDAC patients, PNET patients and Others were more likely to have Medium/High risk scores for Financial Strain Risk (6% vs 6.5% and 8.5%, respectively) and Food Insecurity (1.7% vs 3.8% and 3.2%) (Table). There was no statistically significant difference in median medical debt and need of financial assistance between the groups. Among PDAC patients, there were differences in distributions among race, primary language spoken, and patient electronic record access depending on Financial Risk categories. African American patients had the greatest proportion of Medium/High risk (12.9%) compared to Asian (4.4%) and White (5.8%) patients (P&lt;0.001). Among patients with English as their primary language, 64.9% had Low Risk scores compared to 27.7% with other primary languages (P&lt;0.001). Patients with electronic record access were more likely to report High Risk scores (6.9%) compared to those without access (1.6%). Conclusions: Per patient responses collected, PDAC patients presenting to our center have lower risk of financial strain and similar medical debt levels compared to the remainder of cancer patients. Among PDAC patients, higher financial strain was experienced by non-whites (7.9% vs 5.8%). Diagnosis PDAC (N=2607) PNET (N=449) Other (N=84983) p value Current Financial Risk (Grouped) &lt; 0.001 - Low Risk 1662 (63.8%) 304 (67.7%) 59298 (69.8%) - Medium/High Risk 157 (6.0%) 29 (6.5%) 7209 (8.5%) - Unknown/Missing 788 (30.2%) 116 (25.8%) 18476 (21.7%) Food Insecurity Risk &lt; 0.001 - Low Risk 1764 (67.7%) 308 (68.6%) 63235 (74.4%) - Medium Risk 45 (1.7%) 17 (3.8%) 2703 (3.2%) - Unknown/Missing 798 (30.6%) 124 (27.6%) 19045 (22.4%) Median Active Debt in USD 1102.230 1020.010 1141.135 0.593

Association Between LACE+ Index Risk Category and 90-Day Mortality After Stroke.

A higher LACE+ index risk category (defined as LACE+ score ≥78) typically calculated before hospital discharge has been associated with increased risk of unplanned 30-day hospital readmissions and early death after hospital discharge. However, its utility to predict poststroke mortality is unknown. Here, we examined whether the LACE+ index risk category assessed at both discharge (dLACE+) and admission (aLACE+) was associated with 90-day mortality after stroke. We retrospectively analyzed 2,729 consecutive patients who presented with ischemic or hemorrhagic strokes, included in an institutional stroke registry between January 2018 and December 2021. The primary outcome of interest was 90-day mortality after the index hospitalization. Patients were categorized as high-risk (≥78), medium-to-high-risk (59-77), and low-to-medium-risk (0-58) according to the LACE+ as automatically calculated at admission and discharge. Analyses were performed on the entire cohort, as well as stratified according to acute ischemic stroke and hemorrhagic stroke diagnosis. Among patients who completed 90-day follow-up, the mortality rate was 24.3% (576/2368). In the Kaplan-Meier analysis, the high-risk aLACE+ group had the highest 90-day mortality rate as compared with low-to-medium-risk and medium-to-high-risk groups (p < 0.001). In a fully adjusted multivariable Cox-regression, the 90-day hazards of death were significantly greater among participants in a high-risk aLACE+ (aHR 1.7, 95% CI 1.080-2.742, p = 0.022) and medium-to-high-risk aLACE+ categories (aHR 1.4, 95% CI 1.141-1.778, p = 0.002) as compared with participants in the low-to-medium-risk aLACE+ category. Results were overall similar for dLACE+. The LACE+ calculated at both admission and discharge admission identified patients with stroke at increased risk for 90-day mortality. Future studies are warranted to determine whether LACE+ score-based risk stratification can be used to devise early interventions to mitigate the risk for death.

Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach.

Major Depressive Disorder (MDD) is a prevalent mental health condition that often begins in adolescence, with significant long-term implications. Indicated prevention programs targeting adolescents with mild symptoms have shown efficacy, yet the methods for identifying at-risk individuals need improvement. This study aims to evaluate the utility of Partial Least Squares Regression (PLSR) in predicting the onset of MDD among non-depressed adolescents, compared to traditional screening methods. The study recruited 1462 Portuguese adolescents aged 13-16, who were assessed using various self-report measures and followed for two years. Participants were randomly divided into training (70%, N=1023) and testing (30%, N=439) samples. PLSR models were developed to predict the occurrence of a major depressive episode (MDE) within two years, using 331 variables. The model's performance was compared to the Children's Depression Inventory (CDI) in predicting MDE onset. The best-fitting PLSR model with two components explained 19.1% and 16.9% of the variance in the training and testing samples, respectively, significantly outperforming the CDI, which explained 7.7% of the variance. The area under the ROC curve was 0.78 for PLSR, compared to 0.71 for CDI. An empirically derived cut-off point was used to create dichotomous risk categories, and it showed a significant difference in MDE rates between predicted high-risk and low-risk groups. The balanced accuracy of the PLSR model was 0.77, compared to 0.65 for the CDI method. The PLSR model effectively identified adolescents at risk for developing MDD, demonstrating superior predictive power over the CDI. This study supports the potential utility of ML techniques (e.g., PLSR) in enhancing early identification and prevention efforts for adolescent depression.

The interconnected nature of multiple threats is impacting freshwater biodiversity.

Freshwater biodiversity is in crisis across the globe: the significant extraction, modification and pollution of freshwater resources puts these communities and systems at great risk. Here, using probabilistic network analysis and International Union for the Conservation of Nature threat data, we show that globally and across all taxonomic groups and geographical regions, threats to freshwater species are interconnected and do not occur in isolation. However, we also find that species in higher risk categories are more acutely threatened by single, dominant threats as compared with species at lower risk of global extinction. Determining when and which species are threatened by isolated threats or a suite of co-occurring threats provides crucial insights for the design of effective freshwater conservation strategies.