Development and Assessment of an Artificial Intelligence-Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology Practices.

Importance: Most dermatologic cases are initially evaluated by nondermatologists such as primary care physicians (PCPs) or nurse practitioners (NPs). Objective: To evaluate an artificial intelligence (AI)-based tool that assists with diagnoses of dermatologic conditions. Design, Setting, and Participants: This multiple-reader, multiple-case diagnostic study developed an AI-based tool and evaluated its utility. Primary care physicians and NPs retrospectively reviewed an enriched set of cases representing 120 different skin conditions. Randomization was used to ensure each clinician reviewed each case either with or without AI assistance; each clinician alternated between batches of 50 cases in each modality. The reviews occurred from February 21 to April 28, 2020. Data were analyzed from May 26, 2020, to January 27, 2021. Exposures: An AI-based assistive tool for interpreting clinical images and associated medical history. Main Outcomes and Measures: The primary analysis evaluated agreement with reference diagnoses provided by a panel of 3 dermatologists for PCPs and NPs. Secondary analyses included diagnostic accuracy for biopsy-confirmed cases, biopsy and referral rates, review time, and diagnostic confidence. Results: Forty board-certified clinicians, including 20 PCPs (14 women [70.0%]; mean experience, 11.3 [range, 2-32] years) and 20 NPs (18 women [90.0%]; mean experience, 13.1 [range, 2-34] years) reviewed 1048 retrospective cases (672 female [64.2%]; median age, 43 [interquartile range, 30-56] years; 41 920 total reviews) from a teledermatology practice serving 11 sites and provided 0 to 5 differential diagnoses per case (mean [SD], 1.6 [0.7]). The PCPs were located across 12 states, and the NPs practiced in primary care without physician supervision across 9 states. The NPs had a mean of 13.1 (range, 2-34) years of experience and practiced in primary care without physician supervision across 9 states. Artificial intelligence assistance was significantly associated with higher agreement with reference diagnoses. For PCPs, the increase in diagnostic agreement was 10% (95% CI, 8%-11%; P < .001), from 48% to 58%; for NPs, the increase was 12% (95% CI, 10%-14%; P < .001), from 46% to 58%. In secondary analyses, agreement with biopsy-obtained diagnosis categories of maglignant, precancerous, or benign increased by 3% (95% CI, -1% to 7%) for PCPs and by 8% (95% CI, 3%-13%) for NPs. Rates of desire for biopsies decreased by 1% (95% CI, 0-3%) for PCPs and 2% (95% CI, 1%-3%) for NPs; the rate of desire for referrals decreased by 3% (95% CI, 1%-4%) for PCPs and NPs. Diagnostic agreement on cases not indicated for a dermatologist referral increased by 10% (95% CI, 8%-12%) for PCPs and 12% (95% CI, 10%-14%) for NPs, and median review time increased slightly by 5 (95% CI, 0-8) seconds for PCPs and 7 (95% CI, 5-10) seconds for NPs per case. Conclusions and Relevance: Artificial intelligence assistance was associated with improved diagnoses by PCPs and NPs for 1 in every 8 to 10 cases, indicating potential for improving the quality of dermatologic care.

A deep learning system for differential diagnosis of skin diseases.

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions.

ClusterCAD: a computational platform for type I modular polyketide synthase design.

ClusterCAD is a web-based toolkit designed to leverage the collinear structure and deterministic logic of type I modular polyketide synthases (PKSs) for synthetic biology applications. The unique organization of these megasynthases, combined with the diversity of their catalytic domain building blocks, has fueled an interest in harnessing the biosynthetic potential of PKSs for the microbial production of both novel natural product analogs and industrially relevant small molecules. However, a limited theoretical understanding of the determinants of PKS fold and function poses a substantial barrier to the design of active variants, and identifying strategies to reliably construct functional PKS chimeras remains an active area of research. In this work, we formalize a paradigm for the design of PKS chimeras and introduce ClusterCAD as a computational platform to streamline and simplify the process of designing experiments to test strategies for engineering PKS variants. ClusterCAD provides chemical structures with stereochemistry for the intermediates generated by each PKS module, as well as sequence- and structure-based search tools that allow users to identify modules based either on amino acid sequence or on the chemical structure of the cognate polyketide intermediate. ClusterCAD can be accessed at https://clustercad.jbei.org and at http://clustercad.igb.uci.edu.

Alteration of Polyketide Stereochemistry from anti to syn by a Ketoreductase Domain Exchange in a Type I Modular Polyketide Synthase Subunit.

Polyketide natural products have broad applications in medicine. Exploiting the modular nature of polyketide synthases to alter stereospecificity is an attractive strategy for obtaining natural product analogues with altered pharmaceutical properties. We demonstrate that by retaining a dimerization element present in LipPks1+TE, we are able to use a ketoreductase domain exchange to alter α-methyl group stereochemistry with unprecedented retention of activity and simultaneously achieve a novel alteration of polyketide product stereochemistry from anti to syn. The substrate promiscuity of LipPks1+TE further provided a unique opportunity to investigate the substrate dependence of ketoreductase activity in a polyketide synthase module context.

Broad substrate specificity of the loading didomain of the lipomycin polyketide synthase.

LipPks1, a polyketide synthase subunit of the lipomycin synthase, is believed to catalyze the polyketide chain initiation reaction using isobutyryl-CoA as a substrate, followed by an elongation reaction with methylmalonyl-CoA to start the biosynthesis of antibiotic α-lipomycin in Streptomyces aureofaciens Tü117. Recombinant LipPks1, containing the thioesterase domain from the 6-deoxyerythronolide B synthase, was produced in Escherichia coli, and its substrate specificity was investigated in vitro. Surprisingly, several different acyl-CoAs, including isobutyryl-CoA, were accepted as the starter substrates, while no product was observed with acetyl-CoA. These results demonstrate the broad substrate specificity of LipPks1 and may be applied to producing new antibiotics.

Compliance therapy: an intervention to improve inpatients' attitudes toward treatment.

Nonadherence to prescription medication is the leading cause of relapse or recurrence of psychotic illness. Literature has shown that compliance therapy, a brief intervention based on motivational interviewing and cognitive approaches, can lead to improved attitudes, adherence to treatment, and insight. This descriptive study aimed to examine the effectiveness of compliance therapy on treatment adherence. The 69 participants were patients of the same ward, either referred for patient education by the psychiatrist or selected from a convenience sample of patients on the clinical pathway of relapsed schizophrenia or major depression. Compliance therapy was conducted individually or in small groups of 2 to 3 patients. The shortened version of the Drug Attitude Inventory (DAI-10), along with a rating scale adapted from another study, was used to measure attitude and the rate of adherence before and after therapy. Patients in both individual and group sessions showed significant improvements in attitude. Those with six or more admissions had slightly less significant improvement, and those with personality disorder or substance abuse showed no significant improvement. Compliance therapy benefits patients and improves their attitude toward treatment. Psychiatric nurses could be trained in this clinical intervention to enable them to conduct compliance therapy in the hospital or in the community.

Nurses' attitudes toward people with mental illnesses in a psychiatric hospital in Singapore.

A positive attitude toward people with mental illnesses is a necessary dimension of psychiatric nursing practice. Although studies have been conducted in Israel and Australia to examine the attitudes of nurses toward people with mental illnesses, no such study had been conducted in Singapore. This study explored the factors influencing nurses' attitudes toward and their involvement in caring for people with mental illnesses, and sought to establish the relevance and effects of mental health training on enhancing the nurses' positive attitudes. The study used a descriptive, self-administered questionnaire, which contained 24 statements scored on a Likert scale. Factors related to attitudes toward people with mental illnesses included age, professional qualification, years of psychiatric nursing experience, type of ward, and designation (i.e., position within the institution). The questionnaires were given to all of the local nurses working in a psychiatric hospital in Singapore, and 96% returned the questionnaires. Results showed that nurses with any of the following characteristics had a more positive attitude: age group of 31 to 50; professional qualification of advanced diploma in mental health nursing, nursing degree, or post-basic certificate; or more than 10 years of psychiatric nursing experience. In addition, nursing officers had a more positive attitude than staff nurses or assistant nurses, and nurses working in the short-stay wards had more positive attitudes than those working in the long-stay wards.