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Key Clinical Message: This article reports a case of mediastinal lymph node tuberculosis with no obvious symptoms and a concealed focus. This patient, a 33‐year‐old male, suffered from pain behind the sternum after eating. He underwent three gastroscopic examinations and two fine needle punctures guided by ultrasound gastroscopy but was not diagnosed. Chest‐enhanced CT revealed a mediastinal mass compressing the adjacent esophagus, suggesting the possibility of enlarged lymph nodes. Furthermore, T cells from patients infected with tuberculosis tested positive. Ultrasound bronchoscopy revealed enlarged lymph nodes in area 7, and then EBUS‐TBNA was performed in that region. Only a few scattered lymphocytes and necrotic tissue were found under the biopsy microscope. The EBUS‐TBNA biopsy Xpert MTB/RIF showed low positive results, and the EBUS‐TBNA puncture fluid Xpert MTB/RIF was positive. Therefore, he was diagnosed with mediastinal lymph node tuberculosis. After antituberculosis treatment with the 2HREZ/10HRE regimen, the patient's pain behind the sternum gradually alleviated, and the enlarged mediastinal lymph nodes gradually narrowed. [ABSTRACT FROM AUTHOR]

Cytochrome P450 (CYP) enzymes are involved in the metabolism of approximately 75% of marketed drugs. Inhibition of the major drug‐metabolizing P450s could alter drug metabolism and lead to undesirable drug–drug interactions. Therefore, it is of great significance to explore the inhibition of P450s in drug discovery. Currently, machine learning including deep learning algorithms has been widely used for constructing in silico models for the prediction of P450 inhibition. These models exhibited varying predictive performance depending on the use of machine learning algorithms and molecular representations. This leads to the difficulty in the selection of appropriate models for practical use. In this study, we systematically evaluated the conventional machine learning and deep learning models for three major P450 enzymes, CYP3A4, CYP2D6, and CYP2C9 from several perspectives, such as algorithms, molecular representation, and data partitioning strategies. Our results showed that the XGBoost and CatBoost algorithms coupled with the combined fingerprint/physicochemical descriptor features exhibited the best performance with Area Under Curve (AUC) of 0.92, while the deep learning models were generally inferior to the conventional machine learning models (average AUC reached 0.89) on the same test sets. We also found that data volume and sampling strategy had a minor effect on model performance. We anticipate that these results are helpful for the selection of molecular representations and machine learning/deep learning algorithms in the P450 model construction and the future model development of P450 inhibition. Inhibition of the major drug‐metabolizing cytochrome P450 enzymes could lead to serious adverse reactions. Currently, a variety of machine learning models have been developed for the prediction of P450 inhibitors, but a detailed comparison is lacking. Different algorithms and representations were used to build the predictive models, and their performance was compared here. [ABSTRACT FROM AUTHOR]

The precisely modulated synthesis of programmable light‐emitting materials remains a challenge. To address this challenge, we construct four tetraphenylethylene‐based supramolecular architectures (SA, SB, SC, and SD), revealing that they exhibit higher electrochemiluminescence (ECL) intensities and efficiencies than the tetraphenylethylene monomer and can be classified as highly efficient and precisely modulated intramolecular aggregation–induced electrochemiluminescence (PI‐AIECL) systems. The best‐performing system (SD) shows a high ECL cathodic efficiency exceeding that of the benchmark tris(2,2′‐bipyridyl)ruthenium(II) chloride in aqueous solution by nearly six‐fold. The electrochemical characterization of these architectures in an organic solvent provides deeper mechanistic insights, revealing that SD features the lowest electrochemical band gap. Density functional theory calculations indicate that the band gap of the guest ligand in the SD structure is the smallest and most closely matched to that of the host scaffold. Finally, the SD system is used to realize ECL‐based cysteine detection (detection limit=14.4 nM) in real samples. Thus, this study not only provides a precisely modulated supramolecular strategy allowing chromophores to be controllably regulated on a molecular scale, but also inspires the programmable synthesis of high‐performance aggregation‐induced electrochemiluminescence emitters. [ABSTRACT FROM AUTHOR]

Ordos Basin, a large lacustrine basin in the Triassic, possesses amounts of conventional and unconventional petroleum. In the basin, the Chang 7 shales have been studied extensively. Recent studies suggested that the crude oils accumulated in the Triassic Chang 9–8 should be mainly sourced by the Chang 9 shales [including the delta plain shale (DPS), delta front shale (DFS), and deep lake shale (DLS)]. The source rock (SR) potential, hydrocarbon generation (HG), and hydrocarbon expulsion (HE) characters of the Chang 9 shales should be analysed to make a comprehensive source rock evaluation. The shales are evaluated as excellent source rock potential, and the DLS is evaluated as the best potential. The HG–HE of the shales are analysed by utilizing two (closed and open systems) pyrolysis experiments. The DLS activation energy (Ea) shows the narrowest distribution and has the lowest average value of 219.00 kJ/mol. The HG onsets and total amounts of the DFS, DPS, and DLS have been determined (165 Ma and 200 × 103 t/km2, 157 Ma and 305 × 103 t/km2, 154 Ma and 430 × 103 t/km2), and the peak generation of the shales happened at 125–117 Ma, 127–100 Ma, and 125–100 Ma, respectively. The Chang 9 DFS, DPS, and DLS have reached the HE threshold at depths of 2,250, 2,500, and 2050 m, respectively, indicating large amounts of hydrocarbons have been expelled into the Lower Yanchang Formation (LYF). The tight sandstone oil reservoirs (TSORs) are mainly located in the areas where the organic‐rich shales are developed, especially in the northeastern study area. The areas between high‐value zones of source rock properties (e.g., thickness, Ro, total organic carbon, HG–HE intensity) are the sweet spots. [ABSTRACT FROM AUTHOR]

Z‐nucleic acid binding protein (ZBP)1 (also known as DAI and DLM1) is a pathogen sensor activated by double‐strand character RNA to recruit receptor‐interacting protein (RIP) kinase via a RIP homotypic interaction motif. The activation of receptor‐interacting protein kinase (RIPK)3 and initiation of virus‐induced necroptosis were initially reported in a landmark publication Upton et al. (Cell Host Microbe11: 290, 2012) employing the DNA virus murine cytomegalovirus (MCMV). M45‐encoded viral inhibitor of RIP activation prevents virus‐induced necroptosis. Additional virus‐encoded suppressors of necroptosis were then identified, including herpes simplex virus ICP6 and vaccinia virus E3L. Caspase‐8 suppressors encoded by these DNA viruses block apoptosis, unleashing necroptosis mediated through Z‐nucleic acid binding protein 1 (ZBP1) recruitment of RIPK3. These studies all utilized ZBP1‐deficient mice generated by the Akira Lab (Zbp1−/− AK) to bring the significance of virus‐induced necroptosis to light. C57BL/6 mice were chosen as controls based on the assumption that mutant mice were congenic; however, these mice were recently found to display an unexpected innate immune deficit, lacking C57BL/6‐specific NK1.1 and Ly49H natural killer cell subpopulations important in the early control of MCMV infection. Short nucleotide polymorphism analysis of Zbp1−/− AK breeders revealed a mixed genetic background (~ 71% C57BL/6 DNA and ~ 29% 129). Even though this level of 129 strain background does not alter ZBP1 cell‐autonomous function as a sensor and mediator of necroptosis, it confounds innate immune response characteristics. In the future, genetic background must be carefully controlled before implicating ZBP1 function in response characteristics that shape immunity, inflammation, metabolism, and pathogenesis. [ABSTRACT FROM AUTHOR]

The Ordos Basin, a large mainly non‐marine basin, is an important petroliferous basin in northern China. The Triassic Chang 9 shales, including the delta plain shale (DPS), delta front shale (DFS), and deep lake shale (DLS), are proven to be the main source rocks in the lower Yanchang Formation (LYF). This study investigates the source rock potential, palaeoenvironment, and organic matter (OM) source. According to the OM thermal maturity, abundance, and type, the shales have excellent source rock potential. The DLS has the highest quality with average TOC of 7.08%, S1 + S2 of 14.41 mg HC/g rock, and Type II1–II2. According to correlations of Rb/Sr versus Sr/Cu, the shales were mainly deposited in arid conditions. Based on the major element (S), trace elements (V, Ni, Co, Cr, U, and Th), rare earth element (Ce) concentrations, and biomarkers (Pr, Ph, dibenzofuran, fluorene, and dibenzothiophene), redox conditions for the DPS, DFS, and DLS were oxidizing, sub‐oxidizing to sub‐reducing, and reducing, respectively. The palaeosalinity was fresh‐ to brackish‐water, ascertained by the trace elements (Sr and Ba) and biomarkers (gammacerane). The water stratification was controlled by the temperature changes and the palaeosalinity differences, and the maximum water depth was ~100 m during the Chang 9 deposition. Aquatic microorganisms (e.g., algae, bacteria, and phytoplankton) were the main organic matter sources for the shales, mixed together with terrigenous plants in DPS and DFS. The DLS has the most aquatic microorganisms and has the least higher plant fragments, supported by the higher TOC (up to 14%), lowest vitrinite contents (Avg. = 14.1%), and highest liptinite contents (Avg. = 59.5%). Laterally, the DPS, DFS, and DLS were distributed successively from the lake margin to the depocentre. Vertically, the DLS, DFS, and DPS were developed successively from the bottom lake to the upper lake. The DLS was mainly formed in an environment of high‐level sapropelinite and favourable preservation conditions, and the DPS was mainly formed in an environment of high‐level terrigenous inputs and poor preservation conditions, while the DFS was mainly formed in an environment of medium‐level of sapropelinite productivity and terrigenous inputs and fair preservation conditions. The DPS and DFS were formed during both the early stage (shallow lake) and the late stage (deep lake) of the Chang 9 deposition, while the DLS was only developed in the deep lake during the late Chang 9 deposition. The high‐level sapropelinite production and favourable preservation controlled the formation of the organic‐rich shales, especially for the organic‐rich DLS. [ABSTRACT FROM AUTHOR]