Current passive case-finding policies have not resulted in the expected decline in tuberculosis incidence. Recognition of the variety of disease pathways experienced by individuals with tuberculosis highlights how many are not served by the current prevention and care system, and how much transmission is missed.

Fusarium solani, as an opportunistic pathogen, can infect individuals with immunosuppression, neutropenia, hematopoietic stem cell transplantation (HSCT), or other high-risk factors, leading to invasive or localized infections. Particularly in patients following allogeneic HSCT, Fusarium solani is more likely to cause invasive or disseminated infections. This study focuses on a pediatric patient who underwent HSCT for severe aplastic anemia. Although initial blood cultures were negative, an abnormality was detected in the 1,3-β-D-glucan test (G test) post-transplantation. To determine the causative agent, blood samples were subjected to metagenomic next-generation sequencing (mNGS) and blood cultures simultaneously. Surprisingly, the results of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and mNGS differed slightly, with mNGS identifying Nectria haematonectria, while MALDI-TOF MS based on culture showed Fusarium solani. To clarify the results, Sanger sequencing was performed for further detection, and the results were consistent with those of MALDI-TOF MS. Since the accuracy of Sanger sequencing is higher than that of mNGS, the diagnosis was revised to invasive Fusarium solani infection. With advancements in technology, various detection methods for invasive fungi have been developed in recent years, such as mNGS, which has high sensitivity. While traditional methods may be time-consuming, they are important due to their high specificity. Therefore, in clinical practice, it is essential to utilize both traditional and novel detection methods in a complementary manner to enhance the diagnosis of invasive fungal infections.

OBJECTIVE: Psychosis-associated diagnostic codes are increasingly being utilized as case definitions for electronic health record (EHR)-based algorithms to predict and detect psychosis. However, data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis.
METHODS: Using EHRs at 3 health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into 5 higher-order groups. 1133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings.
RESULTS: PPVs across all diagnostic groups and hospital systems exceeded 70%: Mass General Brigham 0.72 [95% CI 0.68-0.77], Boston Children\'s Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62).
CONCLUSIONS: We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the case definitions used in the development of risk prediction models designed to predict or detect undiagnosed psychosis.

BACKGROUND: Digital phenotyping has seen a broad increase in application across clinical research; however, little research has implemented passive assessment approaches for suicide risk detection. There is a significant potential for a novel form of digital phenotyping, termed screenomics, which captures smartphone activity via screenshots.
OBJECTIVE: This paper focuses on a comprehensive case review of 2 participants who reported past 1-month active suicidal ideation, detailing their passive (ie, obtained via screenomics screenshot capture) and active (ie, obtained via ecological momentary assessment [EMA]) risk profiles that culminated in suicidal crises and subsequent psychiatric hospitalizations. Through this analysis, we shed light on the timescale of risk processes as they unfold before hospitalization, as well as introduce the novel application of screenomics within the field of suicide research.
METHODS: To underscore the potential benefits of screenomics in comprehending suicide risk, the analysis concentrates on a specific type of data gleaned from screenshots-text-captured prior to hospitalization, alongside self-reported EMA responses. Following a comprehensive baseline assessment, participants completed an intensive time sampling period. During this period, screenshots were collected every 5 seconds while one\'s phone was in use for 35 days, and EMA data were collected 6 times a day for 28 days. In our analysis, we focus on the following: suicide-related content (obtained via screenshots and EMA), risk factors theoretically and empirically relevant to suicide risk (obtained via screenshots and EMA), and social content (obtained via screenshots).
RESULTS: Our analysis revealed several key findings. First, there was a notable decrease in EMA compliance during suicidal crises, with both participants completing fewer EMAs in the days prior to hospitalization. This contrasted with an overall increase in phone usage leading up to hospitalization, which was particularly marked by heightened social use. Screenomics also captured prominent precipitating factors in each instance of suicidal crisis that were not well detected via self-report, specifically physical pain and loneliness.
CONCLUSIONS: Our preliminary findings underscore the potential of passively collected data in understanding and predicting suicidal crises. The vast number of screenshots from each participant offers a granular look into their daily digital interactions, shedding light on novel risks not captured via self-report alone. When combined with EMA assessments, screenomics provides a more comprehensive view of an individual\'s psychological processes in the time leading up to a suicidal crisis.

UNASSIGNED: Early detection of psychosis is critical for improving outcomes. Algorithms to predict or detect psychosis using electronic health record (EHR) data depend on the validity of the case definitions used, typically based on diagnostic codes. Data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis.
UNASSIGNED: Using EHRs at three health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into five higher-order groups. 1,133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings.
UNASSIGNED: PPVs across all diagnostic groups and hospital systems exceeded 70%: Massachusetts General Brigham 0.72 [95% CI 0.68-0.77], Boston Children\'s Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62).
UNASSIGNED: We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the development of risk prediction models designed to predict or detect undiagnosed psychosis.

The aims of this study were to investigate the potential of utilising molecularly imprinted polycarbazole layers to detect highly toxic picric acid (PA) and to provide information about their performance. Quantum chemical calculations showed that strong interactions occur between PA and carbazole (bond energy of approximately 31 kJ/mol), consistent with the theoretical requirements for effective molecular imprinting. The performance of the sensors, however, was found to be highly limited, with the observed imprinting factor values for polycarbazole (PCz) layers being 1.77 and 0.95 for layers deposited on Pt and glassy carbon (GC) electrodes, respectively. Moreover, the molecularly imprinted polymer (MIP) layers showed worse performance than unmodified Pt or GC electrodes, for which the lowest limit of detection (LOD) values were determined (LOD values of 0.09 mM and 0.26 mM, respectively, for bare Pt and MIP PCz/Pt, as well as values of 0.11 mM and 0.57 mM for bare GC and MIP PCz/GC). The MIP layers also showed limited selectivity and susceptibility to interfering agents. An initial hypothesis on the reasons for such performance was postulated based on the common properties of conjugated polymers.

This case probes the potential temporal relationship between pancreatic neuroendocrine tumor (PNET) and depression. This patient has chronic symptoms of depression with no formal diagnosis until within a year of doctors suspecting her diagnosis of pancreatic cancer. An excisional biopsy confirmed a grade 1 neuroendocrine tumor (NET) in the pancreas, and postoperative psychiatric consultation confirmed continued elevated depression. This report presents an illustrative example of the ongoing research questions surrounding the relationship between the timing of a depression diagnosis and a PNET diagnosis. The depression-before-diagnosis relationship in pancreatic cancer patients is an observation that warrants further studies as depression could be a valuable early warning sign of pancreatic cancer.

The present paper proposes an eclectic model for examining the cognitive load involved in detecting deception that benefits from the acoustic dimension as an exercise in cognitive forensic linguistics. The corpus used is composed of the legal confession transcripts of the Breonna Taylor\'s Case, a 26-year-old African-American woman worker who was shot dead by police officers in Louisville, Ky., in March 2020 during a crackdown on her apartment. The dataset comprises transcripts and recordings of the persons involved in the shooting event but have given unclear charges, and those accused of contributory negligence due to wanton misfiring. The data is analyzed based on the video interviews and reaction times (RT) as an application of the model proposed. The findings reveal that the episodes chosen and how they are analyzed exhibit that the modified ADCM along with the acoustic dimension provide a clear picture of cognitive load management in the course of constructing and producing lies.

Accurately detecting early developmental stages of insect pests (larvae) from off-the-shelf stereo camera sensor data using deep learning holds several benefits for farmers, from simple robot configuration to early neutralization of this less agile but more disastrous stage. Machine vision technology has advanced from bulk spraying to precise dosage to directly rubbing on the infected crops. However, these solutions primarily focus on adult pests and post-infestation stages. This study suggested using a front-pointing red-green-blue (RGB) stereo camera mounted on a robot to identify pest larvae using deep learning. The camera feeds data into our deep-learning algorithms experimented on eight ImageNet pre-trained models. The combination of the insect classifier and the detector replicates the peripheral and foveal line-of-sight vision on our custom pest larvae dataset, respectively. This enables a trade-off between the robot\'s smooth operation and localization precision in the pest captured, as it first appeared in the farsighted section. Consequently, the nearsighted part utilizes our faster region-based convolutional neural network-based pest detector to localize precisely. Simulating the employed robot dynamics using CoppeliaSim and MATLAB/SIMULINK with the deep-learning toolbox demonstrated the excellent feasibility of the proposed system. Our deep-learning classifier and detector exhibited 99% and 0.84 accuracy and a mean average precision, respectively.

BACKGROUND: The COVID-19 pandemic heavily impacted many low- and middle-income countries (LMICs), such as Peru, overwhelming their health systems. Rapid antigen detection self-tests for SARS-CoV-2, the virus that causes COVID-19, have been proposed as a portable, safe, affordable, and easy-to-perform approach to improve early detection and surveillance of SARS-CoV-2 in resource-constrained populations where there are gaps in access to health care.
OBJECTIVE: This study aims to explore decision makers\' values and attitudes around SARS-CoV-2 self-testing.
METHODS: In 2021, we conducted a qualitative study in 2 areas of Peru (urban Lima and rural Valle del Mantaro). Purposive sampling was used to identify representatives of civil society groups (RSCs), health care workers (HCWs), and potential implementers (PIs) to act as informants whose voices would provide a proxy for the public\'s attitudes around self-testing.
RESULTS: In total, 30 informants participated in individual, semistructured interviews (SSIs) and 29 informants participated in 5 focus group discussions (FGDs). Self-tests were considered to represent an approach to increase access to testing that both the rural and urban public in Peru would accept. Results showed that the public would prefer saliva-based self-tests and would prefer to access them in their community pharmacies. In addition, information about how to perform a self-test should be clear for each population subgroup in Peru. The tests should be of high quality and low cost. Health-informed communication strategies must also accompany any introduction of self-testing.
CONCLUSIONS: In Peru, decision makers consider that the public would be willing to accept SARS-CoV-2 self-tests if they are accurate, safe to use, easily available, and affordable. Adequate information about the self-tests\' features and instructions, as well as about postuse access to counseling and care, must be made available through the Ministry of Health in Peru.