Bolstering the pipeline for primary care: a proposal from stakeholders in medical education

The Association of American Medical Colleges reports an impending shortage of over 90,000 primary care physicians by the year 2025. An aging and increasingly insured population demands a larger provider workforce. Unfortunately, the supply of US-trained medical students entering primary care residencies is also dwindling, and without a redesign in this country’s undergraduate and graduate medical education structure, there will be significant problems in the coming decades. As an institution producing fewer and fewer trainees in primary care for one of the poorest states in the United States, we propose this curriculum to tackle the issue of the national primary care physician shortage. The aim is to promote more recruitment of medical students into family medicine through an integrated 3-year medical school education and a direct entry into a local or state primary care residency without compromising clinical experience. Using the national primary care deficit figures, we calculated that each state medical school should reserve 20–30 primary care (family medicine) residency spots, allowing students to bypass the traditional match after successfully completing a series of rigorous externships, pre-internships, core clerkships, and board exams. Robust support, advising, and personal mentoring are also incorporated to ensure adequate preparation of students. The nation’s health is at risk. With full implementation in allopathic medical schools in 50 states, we propose a long-term solution that will serve to provide more than 1,000–2,700 new primary care providers annually. Ultimately, we will produce happy, experienced, and empathetic doctors to advance our nation’s primary care system

Ketofol for monitored anesthesia care in shoulder arthroscopy and labral repair: a case report

A 21-year-old male (body mass index: 28.3) with a history of asthma and reactive airway disease since childhood underwent left shoulder arthroscopy and labral repair surgery under monitored anesthesia care. Because the procedure was performed in the beach chair position, access to the patient’s airway was limited throughout. To avoid general anesthesia and to limit potential complications associated with monitored anesthesia care, a ketofol admixture was used. This case demonstrates that, in conjunction with regional anesthesia, ketofol may be an acceptable alternative to propofol for maintenance in outpatient orthopedic procedures

CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation

Traffic simulation provides interactive data for the optimization of traffic control policies. However, existing traffic simulators are limited by their lack of scalability and shortage in input data, which prevents them from generating interactive data from traffic simulation in the scenarios of real large-scale city road networks. In this paper, we present \textbf{C}ity \textbf{B}rain \textbf{Lab}, a toolkit for scalable traffic simulation. CBLab consists of three components: CBEngine, CBData, and CBScenario. CBEngine is a highly efficient simulator supporting large-scale traffic simulation. CBData includes a traffic dataset with road network data of 100 cities all around the world. We also develop a pipeline to conduct a one-click transformation from raw road networks to input data of our traffic simulation. Combining CBEngine and CBData allows researchers to run scalable traffic simulations in the road network of real large-scale cities. Based on that, CBScenario implements an interactive environment and a benchmark for two scenarios of traffic control policies respectively, with which traffic control policies adaptable for large-scale urban traffic can be trained and tuned. To the best of our knowledge, CBLab is the first infrastructure supporting traffic control policy optimization in large-scale urban scenarios. CBLab has supported the City Brain Challenge @ KDD CUP 2021. The project is available on GitHub:~\url{https://github.com/CityBrainLab/CityBrainLab.git}.Comment: Accepted by KDD2023 (Applied Data Science Track

A Case of Cushing’s Syndrome due to Ectopic Adrenocorticotropic Hormone Secretion from Esthesioneuroblastoma with Long Term Follow-Up after Resection

We present a case of a 52-year-old male who developed Cushing’s Syndrome due to ectopic adrenocorticotrophic hormone (ACTH) secretion from a large esthesioneuroblastoma (ENB) of the nasal sinuses. The patient initially presented with polyuria, polydipsia, weakness, and confusion. Computed tomography scan of the head and magnetic resonance imaging showed a 7 cm skull base mass centered in the right cribriform plate without sella involvement. Work-up revealed ACTH-dependent hypercortisolemia, which did not suppress appropriately after high-dose dexamethasone. Subsequent imaging of the chest, abdomen, and pelvis did not reveal other possible ectopic sources of ACTH secretion besides the ENB. His hospital course was complicated by severe hypokalemia and hyperglycemia before successful surgical resection of the tumor, the biopsy of which showed ENB. Postoperatively, his ACTH level dropped below the limit of detection. In the ensuing 4 months, he underwent adjuvant chemoradiation with carboplatin and docetaxel with good response and resolution of hypokalemia and hyperglycemia, with no sign of recurrence as of 30 months postoperatively. His endogenous cortisol production is rising but has not completely recovered

GAN-CL: Generative Adversarial Networks for Learning from Complementary Labels

Learning from complementary labels (CLs) is a useful learning paradigm, where the CL specifies the classes that the instance does not belong to, instead of providing the ground truth as in the ordinary supervised learning scenario. In general, although it is less laborious and more efficient to collect CLs compared with ordinary labels, the less informative signal in the complementary supervision is less helpful to learn competent feature representation. Consequently, the final classifier's performance greatly deteriorates. In this article, we leverage generative adversarial networks (GANs) to derive an algorithm GAN-CL to effectively learn from CLs. In addition to the role in original GAN, the discriminator also serves as a normal classifier in GAN-CL, with the objective constructed partly with the complementary information. To further prove the effectiveness of our schema, we study the global optimality of both generator and discriminator for the GAN-CL under mild assumptions. We conduct extensive experiments on benchmark image datasets using deep models, to demonstrate the compelling improvements, compared with state-of-the-art CL learning approaches

LLP-GAN: A GAN-Based Algorithm for Learning From Label Proportions

Learning from label proportions (LLP) is a widespread and important learning paradigm: only the bag-level proportional information of the grouped training instances is available for the classification task, instead of the instance-level labels in the fully supervised scenario. As a result, LLP is a typical weakly supervised learning protocol and commonly exists in privacy protection circumstances due to the sensitivity in label information for real-world applications. In general, it is less laborious and more efficient to collect label proportions as the bag-level supervised information than the instance-level one. However, the hint for learning the discriminative feature representation is also limited as a less informative signal directly associated with the labels is provided, thus deteriorating the performance of the final instance-level classifier. In this article, delving into the label proportions, we bypass this weak supervision by leveraging generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN. Endowed with an end-to-end structure, LLP-GAN performs approximation in the light of an adversarial learning mechanism without imposing restricted assumptions on distribution. Accordingly, the final instance-level classifier can be directly induced upon the discriminator with minor modification. Under mild assumptions, we give the explicit generative representation and prove the global optimality for LLP-GAN. In addition, compared with existing methods, our work empowers LLP solvers with desirable scalability inheriting from deep models. Extensive experiments on benchmark datasets and a real-world application demonstrate the vivid advantages of the proposed approach

A New Extraction Method of Surface Water Based on Dense Time-Sequence Images

Fluctuations in the surface water are indicators of climatic and biological environmental variations. The water index method is the predominant approach for water extraction owing to its simplicity of operation and high efficiency. Recognizing the limitations of individual water indices in extracting water over dense time-sequences, this study introduces a combined water index (CWI) frequency method to improve the water extraction results. The research findings indicate the following: 1) CWI demonstrates superior extraction accuracy for various types of water when compared with other water indices, underscoring its higher precision and broader applicability. 2) By integrating CWI with the water frequency method, we propose an effective approach for dynamically monitoring water. This method accurately reflects changes in water under different conditions within dense time-sequence images. 3) Our results highlight the method's ability to precisely monitor dynamic water changes, efficiently extract various water types from Sentinel-2 data, and its potential for large-scale surface water mapping applications

A Case of Cushing’s Syndrome due to Ectopic Adrenocorticotropic Hormone Secretion from Esthesioneuroblastoma with Long Term Follow-Up after Resection

We present a case of a 52-year-old male who developed Cushing’s Syndrome due to ectopic adrenocorticotrophic hormone (ACTH) secretion from a large esthesioneuroblastoma (ENB) of the nasal sinuses. The patient initially presented with polyuria, polydipsia, weakness, and confusion. Computed tomography scan of the head and magnetic resonance imaging showed a 7 cm skull base mass centered in the right cribriform plate without sella involvement. Work-up revealed ACTH-dependent hypercortisolemia, which did not suppress appropriately after high-dose dexamethasone. Subsequent imaging of the chest, abdomen, and pelvis did not reveal other possible ectopic sources of ACTH secretion besides the ENB. His hospital course was complicated by severe hypokalemia and hyperglycemia before successful surgical resection of the tumor, the biopsy of which showed ENB. Postoperatively, his ACTH level dropped below the limit of detection. In the ensuing 4 months, he underwent adjuvant chemoradiation with carboplatin and docetaxel with good response and resolution of hypokalemia and hyperglycemia, with no sign of recurrence as of 30 months postoperatively. His endogenous cortisol production is rising but has not completely recovered

Bioorthogonal Polymerization of Coiled-Coil Peptides

Peptides capable of forming homotetrameric coiled-coil bundles are utilized as the monomeric building blocks (“bundlemers”) to synthesize hybrid polymers consisting of covalently linked coiled-coil microdomains with regularly spaced ethylene glycol repeats via tetrazine ligation with trans-cyclooctene. We confirm the formation of long, semiflexible rods with a Kuhn length of 6 - 7 nm and a molecular weight of 100 - 3,000 kDa. Polymerization at tetrazine concentrations > 5 mM results in the formation of mechanically robust hydrogels with defined viscoelastic properties through chain entanglements. Copolymerization of coiled-coil peptides with distinct composition and thermal stability gives rise to hydrogels that are thermally tractable. Solid-to-fluid transition occurs when one of the coiled-coil repeats melts. Upon cooling, solid-like properties are partially recovered through intermolecular association of the helical peptides. Overall, tetrazine ligation has enabled the covalent polymerization of self-assembled coiled-coil motifs for the establishment of protein-like linear polymers with unprecedent molecular weight