<b>A Primary Care Guide to the Screening and Pharmacologic Management of Chronic Kidney Disease in People Living With Type 2 Diabetes</b> by Eugene E. Wright (21500539)

“…<p dir="ltr">This paper reports the expert opinions and recommendations made by primary care physicians (PCPs) to optimize screening and management of chronic kidney disease (CKD) associated with diabetes and presents algorithms to provide a practical and simplified guide for PCPs. …”

Data_Sheet_1_Prediction of patient choice tendency in medical decision-making based on machine learning algorithm.pdf by Yuwen Lyu (14330781)

Table_1_Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images.DOCX by Yeo Kyoung Won (15599561)

Table_1_Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.docx by Liyin Zhang (6371999)

Data_Sheet_1_A hybrid energy-based and AI-based screening approach for the discovery of novel inhibitors of JAK3.pdf by Juying Wei (13815403)

MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

“…Theoretical and applied experiments are conducted using four solvers: QBSolv, D-Wave Hybrid binary quadratic model 2, D-Wave Advantage system 4.1, and Gurobi. …”

Table 1_Durable response of primary cardiac lymphoma after autologous stem cell transplantation and sequential CAR-T therapy: a case report and literature review.docx by Ge Wang (56838)

“…Moreover, we propose a structured algorithm that may help optimize the clinical implementation of CAR-T therapy in similar cases. …”

Data Sheet 1_Durable response of primary cardiac lymphoma after autologous stem cell transplantation and sequential CAR-T therapy: a case report and literature review.pdf by Ge Wang (56838)

“…Moreover, we propose a structured algorithm that may help optimize the clinical implementation of CAR-T therapy in similar cases. …”

Image1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF by Sizhuo Yu (11429743)

“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”

Image3_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF by Sizhuo Yu (11429743)

“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”

Image2_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF by Sizhuo Yu (11429743)

“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”

DataSheet1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.pdf by Sizhuo Yu (11429743)

“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”

Flowchart of screening and inclusion. by Jennifer S. Breel (15285263)

“…These results show the potential of ROTEM to detect coagulation abnormalities in patients with infective endocarditis. Existing point-of-care coagulation testing guided algorithms for optimizing perioperative coagulation management possibly need to be adjusted for these high-risk patients undergoing cardiac surgery.…”

Supplementary file 2_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.xlsx by Feng Han (10919)

“…Background<p>Hymenopteran stings (from bees, wasps, and hornets) can trigger severe systemic reactions, especially in tropical regions, risking patient safety and emergency care efficiency. Accurate early risk stratification is essential to guide timely intervention.…”

Image 1_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.png by Feng Han (10919)

“…Background<p>Hymenopteran stings (from bees, wasps, and hornets) can trigger severe systemic reactions, especially in tropical regions, risking patient safety and emergency care efficiency. Accurate early risk stratification is essential to guide timely intervention.…”

Supplementary file 1_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.docx by Feng Han (10919)

“…Background<p>Hymenopteran stings (from bees, wasps, and hornets) can trigger severe systemic reactions, especially in tropical regions, risking patient safety and emergency care efficiency. Accurate early risk stratification is essential to guide timely intervention.…”

Image 2_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.png by Feng Han (10919)

“…Background<p>Hymenopteran stings (from bees, wasps, and hornets) can trigger severe systemic reactions, especially in tropical regions, risking patient safety and emergency care efficiency. Accurate early risk stratification is essential to guide timely intervention.…”