We report a rare case of Sjogren\'s syndrome complicated with Birt-Hogg-Dubé syndrome (BHDS) not previously mentioned in the literature. Further, there is insufficient evidence linking the two diseases. Here, we review existing diagnostic algorithms for diagnosing diffuse cystic lung disease and provide new insights. The patient initially complained of thirst and dry eyes for ten years, and gradually developed shortness of breath. After admission, physical examination showed five missing teeth, decreased respiratory sounds in both lower lungs, and Velcro rales. Computed tomography showed multiple thin-walled cystic lesions in both lungs. Initial xerophthalmia and labial gland biopsy seemed to reveal a pulmonary cystic change associated with Sjogren\'s syndrome. Before discharge, a rash suspected to indicate a fibrofollicular tumor in the neck was observed, and then FLCN variant has been found. The challenges how to clarify the diagnosis of DCLD causes are discussed.

Nevus spilus (NS) is composed of multiple types that characterized by a congenital hyperpigmented patch within variable even superimposed lesions originating from melanocytic lineage cells. The molecular mechanism and classification of diverse NS phenotypes remain unclear. Five children with a phenotype of NS were genotyped by the panel based on next-generation sequencing in this study. DNA from biopsies, blood samples and hair follicle were sequenced to confirm the presence of a somatic mutation. Sequencing results indicated somatic mutation in the gene of NRAS or HRAS in all biopsies from the nevi, and the pathogenic variants were not detected in the samples of blood and hair follicle. This study successfully identified the somatic mutation in five unrelated children with diverse NS phenotypes. Moreover, it provided typical images and differential diagnoses between variable NS phenotypes in clinical, pathological, and genetic features, and first proposed a clinical diagnostic algorithm that contributed to simplifying and optimizing the diagnoses and management of these overlapped diseases.

BACKGROUND: The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status.
METHODS: T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms.
RESULTS: A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively.
CONCLUSIONS: Cforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings.

UNASSIGNED: Differentiating between benign and malignant pulmonary nodules is a diagnostic challenge, and inaccurate detection can result in unnecessary invasive procedures. Cell-free DNA (cfDNA) has been successfully utilized to detect various solid tumors. In this study, we developed a genome-wide approach to explore the characteristics of cfDNA sequencing reads obtained by low-depth whole-genome sequencing (LD-WGS) to diagnose pulmonary nodules.
UNASSIGNED: LD-WGS was performed on cfDNA extracted from 420 plasma samples from individuals with pulmonary nodules that were no more than 30 mm in diameter, as determined by computed tomography (CT). The sequencing read distribution patterns of cfDNA were analyzed and used to establish a model for distinguishing benign from malignant pulmonary nodules.
UNASSIGNED: We proposed the concept of weighted reads distribution difference (WRDD) based on the copy number alterations (CNAs) of cfDNA to construct a benign and malignant diagnostic (BEMAD) algorithm model. In a training cohort of 360 plasma samples, the model achieved an average area under the receiver operating characteristic (ROC) curve (AUC) value of 0.84 in 10-fold cross-validation. The model was validated in an independent cohort of 60 plasma samples, obtaining an AUC value of 0.87. The BEMAD model could distinguish benign from malignant nodules at a sensitivity of 74% and a specificity of 86%. Furthermore, analysis of the critical features of the cfDNA using the BEMAD model identified repeat regions that were associated with microsatellite instability, which is an important indicator of tumorigenesis.
UNASSIGNED: This study provides a novel non-invasive diagnostic approach to discriminate between benign and malignant pulmonary nodules to avoid unnecessary invasive procedures.

BACKGROUND: Aromatic L-amino acid decarboxylase deficiency is a rare neurometabolic disease due to impaired decarboxylation of neurotransmitter precursors to its active form.
METHODS: We retrospectively reviewed 8 cases from 2008 to 2019 with cerebrospinal fluid neurotransmitter analysis performed at our centre. All cases had an elevated urine vanillactic acid and, in most cases, with N-acetylvanilalanine detected. Cerebrospinal fluid analysis showed low downstream metabolites vanillylmandelic acid, homovanillic acid but high 3-O-methyl-L-DOPA, 5-hydroxytryptophan. Cerebrospinal fluid pterins were normal. Genotyping in DDC confirms the diagnosis. Urine organic acid analysis provided the first clue to diagnosis in four of the cases, which then triggered cerebrospinal fluid neurotransmitter and genetic analysis. We also developed a diagnostic decision support system to assist the interpretation of the mass spectrometry data from urine organic acids.
CONCLUSIONS: Urine organic acid could be essential in guiding subsequent investigations for the diagnosis of aromatic L-amino acid decarboxylase deficiency. We propose to screen suspected cases first with urine organic acids, specifically looking for vanillactic acid and N-acetylvanilalanine. Suggestive findings should be followed with target analysis for c.714 + 4A > T in ethnically Chinese patients. The assistive tool allowed expedite interpretation of profile data generated from urine organic acids analysis. It may also reduce interpreter\'s bias when peaks of interest are minor peaks in the spectrum.

The prompt diagnosis of pulmonary tuberculosis (PTB) remains a challenge in clinical practice. The present study aimed to optimize an algorithm for rapid diagnosis of PTB in a real-world setting.
28,171 adult inpatients suspected of having PTB in China were retrospectively analyzed. Bronchoalveolar lavage fluid (BALF) and/or sputum were used for acid-fast bacilli (AFB) smear, Xpert MTB/RIF (Xpert), and culture. A positive mycobacterial culture was used as the reference standard. Peripheral blood mononuclear cells (PBMC) were used for T-SPOT.TB. We analyzed specimen types\' effect on these assays\' performance, determined the number of smears for diagnosing PTB, and evaluated the ability of these assays performed alone, or in combination, to diagnose PTB and nontuberculous mycobacteria (NTM) infections.
Sputum and BALF showed moderate to substantial consistency when they were used for AFB smear or Xpert, with a higher positive detection rate by BALF. 3-4 smears had a higher sensitivity than 1-2 smears. Moreover, simultaneous combination of AFB and Xpert correctly identified 44/51 of AFB+/Xpert+ and 6/7 of AFB+/Xpert- cases as PTB and NTM, respectively. Lastly, when combined with AFB/Xpert sequentially, T-SPOT showed limited roles in patients that were either AFB+ or Xpert+. However, T-SPOTMDC (manufacturer-defined cut-off) showed a high negative predicative value (99.1%) and suboptimal sensitivity (74.4%), and TBAg/PHA (ratio of Mycobacterium tuberculosis-specific antigens to phytohaemagglutinin spot-forming cells, which is a modified method calculating T-SPOT.TB assay results) ≥0.3 demonstrated a high specificity (95.7%) and a relatively low sensitivity (16.3%) in AFB-/Xpert- patients.
Concurrently performing AFB smear (at least 3 smears) and Xpert on sputum and/or BALF could aid in rapid diagnosis of PTB and NTM infections in a real-world high-burden setting. If available, BALF is preferred for both AFB smear and Xpert. Expanding this algorithm, PBMC T-SPOTMDC and TBAg/PHA ratios have a supplementary role for PTB diagnosis in AFB-/Xpert- patients (moderately ruling out PTB and ruling in PTB, respectively). Our findings may also inform policy makers\' decisions regarding prevention and control of TB in a high burden setting.

Liver stiffness measurement (LSM) frequently overestimates the severity of liver fibrosis because of steatosis. However, the impact of the controlled attenuation parameter (CAP) on liver stiffness cutoff values remains unknown; CAP was used to quantify and diagnose the severity of hepatic steatosis. The study was conducted to determine the effect of CAP on liver stiffness cutoff values in chronic hepatitis B (CHB) patients. A retrospective cross-sectional study was performed in liver biopsy-proven CHB patients. The median LSM (kPa) in the elevated CAP group was higher than that in the normal CAP group at the same fibrosis stage. For S2-4, the area under the receiver operating characteristic (AUROC) curve of LSM was 0.78 and 0.72 in the normal and elevated CAP groups, respectively. When a cutoff value of 8.9 kPa was used, the diagnostic accuracy was 77.82% and 63.41% in the normal and elevated CAP groups, respectively. Compared with the alanine transaminase (ALT)-based LSM algorithm, the CAP-based LSM algorithm had a similar correct diagnosis rate (33.64% vs. 33.94%, respectively) but a lower misdiagnosis rate (16.97% vs. 20.30%, respectively). The new CAP-based LSM diagnostic algorithm will improve the diagnostic accuracy of liver fibrosis in CHB patients.

BACKGROUND: Reflectance confocal microscopy (RCM), a noninvasive, real-time technique of computed tomography, has been widely used for pigmentary, inflammatory, and tumor diseases of the skin.
OBJECTIVE: Our main purpose was to analyze the consistency between pathological and RCM characteristics of early-stage mycosis fungoides (MF) and the utility of RCM in the diagnosis of early-stage MF.
METHODS: According to the RCM features of MF in the early stage, the biopsy sites of 40 cases of suspected MF and 20 cases of chronic inflammatory skin diseases clinically were preliminarily located. Histopathologic and immunohistochemical examinations were performed to make a diagnosis based on the diagnostic algorithm proposed by the International Society for Cutaneous Lymphomas.
RESULTS: Among the 60 patients observed, there were 12 confirming cases of MF, 14 suspecting cases, 6 not completely excluding cases, and 28 eliminating cases according to the diagnostic algorithm, as well as characteristics of RCM were typical in 8 cases, suspected in 16 cases, not excluded in 3 cases, and excluded in 33 cases. The kappa value was 0.769 (P < .01), which means there is a strong consistency between the classification by RCM and the diagnosis algorithm. MF in patch stage and plaque stage (IA to IIB) has typical characteristics of RCM, respectively.
CONCLUSIONS: RCM can be used as an objective and convenient auxiliary means to diagnose early-stage MF and may be included as part of the diagnostic algorithm of early-stage MF.