Roseinatronobacter alkalisoli sp. nov., an alkaliphilic bacterium isolated from soda soil, and genome-based reclassification of the genera Rhodobaca and Roseinatronobacter.

The genera Rhodobaca and Roseinatronobacter are phylogenetically related genera within the family Paracoccaceae. Species of these genera were described using 16S rRNA gene-based phylogeny and phenotypic characteristics. However, the 16S rRNA gene identity and phylogeny reveal the controversy of the taxonomic status of these two genera. In this work, we examined the taxonomic positions of members of both genera using 16S rRNA gene phylogeny, phylogenomic analysis and further validated using overall genome-related indexes, including digital DNA-DNA hybridization, average nucleotide identity, average amino acid identity and percentage of conserved proteins. Based on phylogenetic and phylogenomic results, the current four species of the two genera clustered tightly into one clade with high bootstrap values, suggesting that the genus Rhodobaca should be merged with Roseinatronobacter. In addition, a novel species isolated from a soda soil sample collected from Anda City, PR China, and designated as HJB301T was also described. Phenotypic, chemotaxonomic, genomic and phylogenetic properties suggested that strain HJB301T (=CCTCC AB 2021113T=KCTC 82977T) represents a novel species of the genus Roseinatronobacter, for which the name Roseinatronobacter alkalisoli sp. nov. is proposed.

Building and validating an artificial intelligence model to identify tracheobronchopathia osteochondroplastica by using bronchoscopic images.

BACKGROUND: Given the rarity of tracheobronchopathia osteochondroplastica (TO), many young doctors in primary hospitals are unable to identify TO based on bronchoscopy findings. OBJECTIVES: To build an artificial intelligence (AI) model for differentiating TO from other multinodular airway diseases by using bronchoscopic images. DESIGN: We designed the study by comparing the imaging data of patients undergoing bronchoscopy from January 2010 to October 2022 by using EfficientNet. Bronchoscopic images of 21 patients with TO at Anhui Chest Hospital from October 2019 to October 2022 were collected for external validation. METHODS: Bronchoscopic images of patients with multinodular airway lesions (including TO, amyloidosis, tumors, and inflammation) and without airway lesions in the First Affiliated Hospital of Guangzhou Medical University were collected. The images were randomized (4:1) into training and validation groups based on different diseases and utilized for deep learning by convolutional neural networks (CNNs). RESULTS: We enrolled 201 patients with multinodular airway disease (38, 15, 75, and 73 patients with TO, amyloidosis, tumors, and inflammation, respectively) and 213 without any airway lesions. To find multinodular lesion images for deep learning, we utilized 2183 bronchoscopic images of multinodular lesions (including TO, amyloidosis, tumor, and inflammation) and compared them with images without any airway lesions (1733). The accuracy of multinodular lesion identification was 98.9%. Further, the accuracy of TO detection based on the bronchoscopic images of multinodular lesions was 89.2%. Regarding external validation (using images from 21 patients with TO), all patients could be diagnosed with TO; the accuracy was 89.8%. CONCLUSION: We built an AI model that could differentiate TO from other multinodular airway diseases (mainly amyloidosis, tumors, and inflammation) by using bronchoscopic images. The model could help young physicians identify this rare airway disease.

Enantioselective synthesis of chiral α,α-dialkyl indoles and related azoles by cobalt-catalyzed hydroalkylation and regioselectivity switch.

General, catalytic and enantioselective construction of chiral α,α-dialkyl indoles represents an important yet challenging objective to be developed. Herein we describe a cobalt catalyzed enantioselective anti-Markovnikov alkene hydroalkylation via the remote stereocontrol for the synthesis of α,α-dialkyl indoles and other N-heterocycles. This asymmetric C(sp3)-C(sp3) coupling features high flexibility in introducing a diverse set of alkyl groups at the α-position of chiral N-heterocycles. The utility of this methodology has been demonstrated by late-stage functionalization of drug molecules, asymmetric synthesis of bioactive molecules, natural products and functional materials, and identification of a class of molecules exhibiting anti-apoptosis activities in UVB-irradiated HaCaT cells. Ligands play a vital role in controlling the reaction regioselectivity. Changing the ligand from bi-dentate L6 to tridentate L12 enables CoH-catalyzed Markovnikov hydroalkylation. Mechanistic studies disclose that the anti-Markovnikov hydroalkylation involves a migratory insertion process while the Markovnikov hydroalkylation involves a MHAT process.

Sequential CD7 CAR T-Cell Therapy and Allogeneic HSCT without GVHD Prophylaxis.

BACKGROUND: Patients with relapsed or refractory hematologic cancers have a poor prognosis. Chimeric antigen receptor (CAR) T-cell therapy as a bridge to allogeneic hematopoietic stem-cell transplantation (HSCT) has the potential for long-term tumor elimination. However, pre-HSCT myeloablation and graft-versus-host disease (GVHD) prophylaxis agents have toxic effects and could eradicate residual CAR T cells and compromise antitumor effects. Whether the integration of CAR T-cell therapy and allogeneic HSCT can preserve CAR T-cell function and improve tumor control is unclear. METHODS: We tested a novel "all-in-one" strategy consisting of sequential CD7 CAR T-cell therapy and haploidentical HSCT in 10 patients with relapsed or refractory CD7-positive leukemia or lymphoma. After CAR T-cell therapy led to complete remission with incomplete hematologic recovery, patients received haploidentical HSCT without pharmacologic myeloablation or GVHD prophylaxis drugs. Toxic effects and efficacy were closely monitored. RESULTS: After CAR T-cell therapy, all 10 patients had complete remission with incomplete hematologic recovery and grade 4 pancytopenia. After haploidentical HSCT, 1 patient died on day 13 of septic shock and encephalitis, 8 patients had full donor chimerism, and 1 patient had autologous hematopoiesis. Three patients had grade 2 HSCT-associated acute GVHD. The median follow-up was 15.1 months (range, 3.1 to 24.0) after CAR T-cell therapy. Six patients remained in minimal residual disease-negative complete remission, 2 had a relapse of CD7-negative leukemia, and 1 died of septic shock at 3.7 months. The estimated 1-year overall survival was 68% (95% confidence interval [CI], 43 to 100), and the estimated 1-year disease-free survival was 54% (95% CI, 29 to 100). CONCLUSIONS: Our findings suggest that sequential CD7 CAR T-cell therapy and haploidentical HSCT is safe and effective, with remission and serious but reversible adverse events. This strategy offers a feasible approach for patients with CD7-positive tumors who are ineligible for conventional allogeneic HSCT. (Funded by the National Natural Science Foundation of China and the Key Project of Science and Technology Department of Zhejiang Province; ClinicalTrials.gov numbers, NCT04599556 and NCT04538599.).

Topological effect of an intramolecular split G-quadruplex on thioflavin T binding and fluorescence light-up.

In this work, the topological effect on binding interaction between a G-quadruplex and thioflavin T (ThT) ligand was systematically investigated on a platform of an intramolecular split G-quadruplex (Intra-SG). Distinct fluorescence changes from ThT were presented in the presence of distinct split modes of Intra-SG structures and an intriguing phenomenon of target-induced fluorescence light-up occurred for split modes 2 : 10, 5 : 7 and 8 : 4. It was validated that hybridization between the Intra-SG spacer and target did not unfold the G-quadruplex, but facilitated the ThT binding. Moreover, the 3' guanine-rich fragment of Intra-SG was very susceptible to topology variation produced by the bound target strand. Additionally, a bioanalytical method was developed for ultrasensitive gene detection, confirming the utility of the ThT/Intra-SG complex as a universal signal transducer. It is believed that the results and disclosed rules will inspire researchers to develop many new DNA-based signal transducers in the future.

Dual-signal readout sensing of ATP content in single dental pulp stem cells during differentiation via functionalized glass nanopipettes.

Adenosine triphosphate (ATP) is regarded as the "energy currency" in living cells, so real-time quantification of content variation of intracellular ATP is highly desired for understanding some important physiological processes. Due to its single-molecule readout ability, nanopipette sensing has emerged as a powerful technique for molecular sensing. In this study, based on the effect of targeting-aptamer binding on ionic current, and fluorescence resonance energy transfer (FRET), we reported a dual-signal readout nanopipette sensing system for monitoring ATP content variation at the subcellular level. In the presence of ATP, the complementary DNA-modified gold nanoparticles (cDNAs-AuNPs) were released from the inner wall of the nanopipette, which leads to sensitive response variations in ionic current rectification and fluorescence intensity. The developed nanopipette sensor was capable of detecting ATP in single cells, and the fluctuation of ATP content in the differentiation of dental pulp stem cells (DPSCs) was further quantified with this method. The study provides a more reliable nanopipette sensing platform due to the introduction of fluorescence readout signals. Significantly, the study of energy fluctuation during cell differentiation from the perspective of energy metabolism is helpful for differentiation regulation and cell therapy.

Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data.

BACKGROUND: Laryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis and quality of life of patients. Pathological biopsy of suspicious cancerous tissue under the guidance of laryngoscopy is the gold standard for diagnosing LPC. However, this subjective examination largely depends on the skills and experience of laryngologists, which increases the possibility of missed diagnoses and repeated unnecessary biopsies. We aimed to develop and validate a deep convolutional neural network-based Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) for real-time automatically identifying LPC in both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI) images to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists. METHODS: All 31,543 laryngoscopic images from 2382 patients were categorised into training, verification, and test sets to develop, validate, and internal test LPAIDS. Another 25,063 images from five other hospitals were used as external tests. Overall, 551 videos were used to evaluate the real-time performance of the system, and 200 randomly selected videos were used to compare the diagnostic performance of the LPAIDS with that of laryngologists. Two deep-learning models using either WLI (model W) or NBI (model N) images were constructed to compare with LPAIDS. RESULTS: LPAIDS had a higher diagnostic performance than models W and N, with accuracies of 0·956 and 0·949 in the internal image and video tests, respectively. The robustness and stability of LPAIDS were validated in external sets with the area under the receiver operating characteristic curve values of 0·965-0·987. In the laryngologist-machine competition, LPAIDS achieved an accuracy of 0·940, which was comparable to expert laryngologists and outperformed other laryngologists with varying qualifications. CONCLUSIONS: LPAIDS provided high accuracy and stability in detecting LPC in real-time, which showed great potential for using LPAIDS to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.

Distinguishing bronchoscopically observed anatomical positions of airway under by convolutional neural network.

Background: Artificial intelligence (AI) technology has been used for finding lesions via gastrointestinal endoscopy. However, there were few AI-associated studies that discuss bronchoscopy. Objectives: To use convolutional neural network (CNN) to recognize the observed anatomical positions of the airway under bronchoscopy. Design: We designed the study by comparing the imaging data of patients undergoing bronchoscopy from March 2022 to October 2022 by using EfficientNet (one of the CNNs) and U-Net. Methods: Based on the inclusion and exclusion criteria, 1527 clear images of normal anatomical positions of the airways from 200 patients were used for training, and 475 clear images from 72 patients were utilized for validation. Further, 20 bronchoscopic videos of examination procedures in another 20 patients with normal airway structures were used to extract the bronchoscopic images of normal anatomical positions to evaluate the accuracy for the model. Finally, 21 respiratory doctors were enrolled for the test of recognizing corrected anatomical positions using the validating datasets. Results: In all, 1527 bronchoscopic images of 200 patients with nine anatomical positions of the airway, including carina, right main bronchus, right upper lobe bronchus, right intermediate bronchus, right middle lobe bronchus, right lower lobe bronchus, left main bronchus, left upper lobe bronchus, and left lower lobe bronchus, were used for supervised machine learning and training, and 475 clear bronchoscopic images of 72 patients were used for validation. The mean accuracy of recognizing these 9 positions was 91% (carina: 98%, right main bronchus: 98%, right intermediate bronchus: 90%, right upper lobe bronchus: 91%, right middle lobe bronchus 92%, right lower lobe bronchus: 83%, left main bronchus: 89%, left upper bronchus: 91%, left lower bronchus: 76%). The area under the curves for these nine positions were >0.98. In addition, the accuracy of extracting the images via the video by the trained model was 94.7%. We also conducted a deep learning study to segment 10 segment bronchi in right lung, and 8 segment bronchi in Left lung. Because of the problem of radial depth, only segment bronchi distributions below right upper bronchus and right middle bronchus could be correctly recognized. The accuracy of recognizing was 84.33 ± 7.52% by doctors receiving interventional pulmonology education in our hospital over 6 months. Conclusion: Our study proved that AI technology can be used to distinguish the normal anatomical positions of the airway, and the model we trained could extract the corrected images via the video to help standardize data collection and control quality.

Predicting the severity of white matter lesions among patients with cerebrovascular risk factors based on retinal images and clinical laboratory data: a deep learning study.

Background and purpose: As one common feature of cerebral small vascular disease (cSVD), white matter lesions (WMLs) could lead to reduction in brain function. Using a convenient, cheap, and non-intrusive method to detect WMLs could substantially benefit to patient management in the community screening, especially in the settings of availability or contraindication of magnetic resonance imaging (MRI). Therefore, this study aimed to develop a useful model to incorporate clinical laboratory data and retinal images using deep learning models to predict the severity of WMLs. Methods: Two hundred fifty-nine patients with any kind of neurological diseases were enrolled in our study. Demographic data, retinal images, MRI, and laboratory data were collected for the patients. The patients were assigned to the absent/mild and moderate-severe WMLs groups according to Fazekas scoring system. Retinal images were acquired by fundus photography. A ResNet deep learning framework was used to analyze the retinal images. A clinical-laboratory signature was generated from laboratory data. Two prediction models, a combined model including demographic data, the clinical-laboratory signature, and the retinal images and a clinical model including only demographic data and the clinical-laboratory signature, were developed to predict the severity of WMLs. Results: Approximately one-quarter of the patients (25.6%) had moderate-severe WMLs. The left and right retinal images predicted moderate-severe WMLs with area under the curves (AUCs) of 0.73 and 0.94. The clinical-laboratory signature predicted moderate-severe WMLs with an AUC of 0.73. The combined model showed good performance in predicting moderate-severe WMLs with an AUC of 0.95, while the clinical model predicted moderate-severe WMLs with an AUC of 0.78. Conclusion: Combined with retinal images from conventional fundus photography and clinical laboratory data are reliable and convenient approach to predict the severity of WMLs and are helpful for the management and follow-up of WMLs patients.

Differential diagnosis of secondary hypertension based on deep learning.

Secondary hypertension is associated with higher risks of target organ damage and cardiovascular and cerebrovascular disease events. Early aetiology identification can eliminate aetiologies and control blood pressure. However, inexperienced doctors often fail to diagnose secondary hypertension, and comprehensively screening for all causes of high blood pressure increases health care costs. To date, deep learning has rarely been involved in the differential diagnosis of secondary hypertension. Relevant machine learning methods cannot combine textual information such as chief complaints with numerical information such as the laboratory examination results in electronic health records (EHRs), and the use of all features increases health care costs. To reduce redundant examinations and accurately identify secondary hypertension, we propose a two-stage framework that follows clinical procedures. The framework carries out an initial diagnosis process in the first stage, on which basis patients are recommended for disease-related examinations, followed by differential diagnoses of different diseases based on the different characteristics observed in the second stage. We convert the numerical examination results into descriptive sentences, thus blending textual and numerical characteristics. Medical guidelines are introduced through label embedding and attention mechanisms to obtain interactive features. Our model was trained and evaluated using a cross-sectional dataset containing 11,961 patients with hypertension from January 2013 to December 2019. The F1 scores of our model were 0.912, 0.921, 0.869 and 0.894 for primary aldosteronism, thyroid disease, nephritis and nephrotic syndrome and chronic kidney disease, respectively, which are four kinds of secondary hypertension with high incidence rates. The experimental results show that our model can powerfully use the textual and numerical data contained in EHRs to provide effective decision support for the differential diagnosis of secondary hypertension.

Apoptosis inhibition enhances induced pluripotent stem cell generation during T cell reprogramming.

The widespread adoption of chimeric antigen receptor (CAR)-T cell therapy has been hindered by its complex and costly manufacturing process. Induced pluripotent stem cells (iPSCs) have shown promise as a cellular immunotherapy alternative, due to their unlimited self-renewal potential in culture and capacity to differentiate into functional immune cell types. However, it is imperative to carefully select the original cell for iPSC seed preparation, as iPSCs have been found to retain the epigenetic imprint of the original somatic cells. Additionally, the efficiency of reprogramming terminal differentiated cells for immunotherapy must be addressed. Our research highlights the superiority of lymphocyte-origin cells over embryonic stem cells in functional immune cell differentiation. Furthermore, blocking Fas-FasL induced apoptosis in T cells significantly improves iPSC generation. Interestingly, transient Fas suppression in T cells does not alter the expression of Fas in the resulting iPSCs or affect their differentiation potential. This finding brings up new avenues in the field of cellular immunotherapy and provides a solution for creating high-quality and suitable iPSCs for lymphocyte differentiation for immunotherapy purposes.

Establishment and observation of a mouse model of IL-12-CAR-T cell-induced cytokine release syndrome / 中国肿瘤生物治疗杂志

@#[摘 要] 目的：通过构建表达IL-12的小鼠CAR-T细胞，探讨经尾静脉将其输注于小鼠体内建立细胞因子释放综合征（CRS）模型的方法。方法：构建基于靶向鼠源CD19的CAR分子，包装逆转录病毒载体并感染小鼠T细胞构建mCD19-CAR-T、mCD19/IL-12-CAR-T细胞。通过构建小鼠体内胰腺癌Panc02-CD19细胞移植瘤模型，检测mCD19/IL-12-CAR-T细胞的抗肿瘤活性，ELISA法检测两种CAR-T细胞IL-12和IFN-γ分泌水平；经小鼠尾静脉输注mCD19/IL-12-CAR-T 细胞构建CAR-T细胞CRS小鼠模型，流式细胞术检测小鼠血清中IL-6、MCP-1、IL-1、IL-10、TNF-α、IFN-γ等细胞因子的含量，H-E染色法观察荷瘤小鼠肝、脾、肺和肾的病理组织学变化。结果：经过培养扩增的mCD19/IL-12-CAR-T细胞能有效分泌IL-12，CAR阳性率达（56.9±5.4）%；与非靶细胞Panc02或靶细胞Panc02-CD19共培养时，均能高分泌IFN-γ。成功构建小鼠胰腺癌Panc02-CD19细胞移植瘤模型，经小鼠尾静脉注射1×106个mCD19/IL-12-CAR-T细胞能显著抑制移植瘤的生长，但未能诱发严重CRS；输注2×106个mCD19/IL-12-CAR-T细胞后，小鼠出现体质量减轻、血清炎性因子水平升高、组织损伤，最终导致死亡等一系列典型CRS表现。结论：成功构建IL-12-CAR-T细胞诱发的小鼠CRS模型，其稳定性好、重复性高，具有广泛的应用前景。

Oncolytic herpes simplex virus delivery of dual CAR targets of CD19 and BCMA as well as immunomodulators to enhance therapeutic efficacy in solid tumors combined with CAR T cell therapy.

Background: The CAR T-cell therapy is a promising approach to treating hematologic malignancies. However, the application in solid tumors still has many tough challenges, including heterogenicity in antigen expressions and immunosuppressive tumor microenvironment (TME). As a new cancer treatment modality, oncolytic virotherapy can be engineered to circumvent these obstacles for CAR T cell therapy in solid tumors. Methods: In this study, an oHSV T7011 is engineered to drive ectopic expression of dual-antigens, extracellular domains of CD19 and BCMA, on the solid tumor cell surface to be targeted by approved CAR T cells. In addition, multiple immunomodulators, CCL5, IL-12, and anti-PD-1 antibody are also included to modulate the TME. The antitumor activities of T7011 in combination with CD19 or BCMA CAR T-cell were evaluated in vitro and in vivo. Results: The expression of CD19 or BMCA on the tumor cell surface could be detected after T7011 infection. The level of CCL5 in TME was also increased. Efficacy studies demonstrated that combination with T7011 and CAR-TCD19 or CAR-TBCMA cells showed significant synergistic anti-tumor responses in several solid tumor models. Conclusion: These studies indicated that the new generation of oHSV T7011 can be a promising combinational therapy with CD19 or BCMA-specific CAR T cells for the treatment of a broad range of solid tumors.

Enantioselective synthesis of N-alkylindoles enabled by nickel-catalyzed C-C coupling.

Enantioenriched N-alkylindole compounds, in which nitrogen is bound to a stereogenic sp3 carbon, are an important entity of target molecules in the fields of biological, medicinal, and organic chemistry. Despite considerable efforts aimed at inventing methods for stereoselective indole functionalization, straightforward access to a diverse range of chiral N-alkylindoles in an intermolecular catalytic fashion from readily available indole substrates remains an ongoing challenge. In sharp contrast to existing C-N bond-forming strategies, here, we describe a modular nickel-catalyzed C-C coupling protocol that couples a broad array of N-indolyl-substituted alkenes with aryl/alkenyl/alkynyl bromides to produce chiral N-alkylindole adducts in single regioisomeric form, in up to 91% yield and 97% ee. The process is amenable to proceed under mild conditions and exhibit broad scope and high functional group compatibility. Utility is highlighted through late-stage functionalization of natural products and drug molecules, preparation of chiral building blocks.

A novel small molecule RK-019 inhibits FGFR2-amplification gastric cancer cell proliferation and induces apoptosis in vitro and in vivo.

Gastric cancer (GC) is one of the most malignant cancers and is estimated to be fifth in incidence ratio and the third leading cause of cancer death worldwide. Despite advances in GC treatment, poor prognosis and low survival rate necessitate the development of novel treatment options. Fibroblast growth factor receptors (FGFRs) have been suggested to be potential targets for GC treatment. In this study, we report a novel selective FGFR inhibitor, RK-019, with a pyrido [1, 2-a] pyrimidinone skeleton. In vitro, RK-019 showed excellent FGFR1-4 inhibitory activities and strong anti-proliferative effects against FGFR2-amplification (FGFR2-amp) GC cells, including SNU-16 and KATO III cells. Treatment with RK-019 suppressed phosphorylation of FGFR and its downstream pathway proteins, such as FRS2, PLCγ, AKT, and Erk, resulting in cell cycle arrest and induction of apoptosis. Furthermore, daily oral administration of RK-019 could attenuate tumor xenograft growth with no adverse effects. Here, we reported a novel specific FGFR inhibitor, RK-019, with potent anti-FGFR2-amp GC activity both in vitro and in vivo.

Genetically modified CD7-targeting allogeneic CAR-T cell therapy with enhanced efficacy for relapsed/refractory CD7-positive hematological malignancies: a phase I clinical study.

Chimeric antigen receptor (CAR)-T cell therapy against T cell malignancies faces major challenges including fratricide between CAR-T cells and product contamination from the blasts. Allogeneic CAR-T cells, generated from healthy donor T cells, can provide ready-to-use, blast-free therapeutic products, but their application could be complicated by graft-versus-host disease (GvHD) and host rejection. Here we developed healthy donor-derived, CD7-targeting CAR-T cells (RD13-01) with genetic modifications to resist fratricide, GvHD and allogeneic rejection, as well as to potentiate antitumor function. A phase I clinical trial (NCT04538599) was conducted with twelve patients recruited (eleven with T cell leukemia/lymphoma, and one with CD7-expressing acute myeloid leukemia). All patients achieved pre-set end points and eleven proceeded to efficacy evaluation. No dose-limiting toxicity, GvHD, immune effector cell-associated neurotoxicity or severe cytokine release syndrome (grade ≥ 3) were observed. 28 days post infusion, 81.8% of patients (9/11) showed objective responses and the complete response rate was 63.6% (7/11, including the patient with AML). 3 of the responding patients were bridged to allogeneic hematopoietic stem cell transplantation. With a median follow-up of 10.5 months, 4 patients remained in complete remission. Cytomegalovirus (CMV) and/or Epstein-Barr virus (EBV) reactivation was observed in several patients, and one died from EBV-associated diffuse large B-cell lymphoma (DLBCL). Expansion of CD7-negative normal T cells was detected post infusion. In summary, we present the first report of a Phase I clinical trial using healthy donor-derived CD7-targeting allogeneic CAR-T cells to treat CD7+ hematological malignancies. Our results demonstrated the encouraging safety and efficacy profiles of the RD13-01 allogeneic CAR-T cells for CD7+ tumors.

Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods.

Objective: Based on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstructive pulmonary disease, bronchiectasis, pulmonary embolism and pulmonary tuberculosis, and provided assistance for primary physicians. Methods: The method combined convolutional neural network (CNN) and long-short-term memory network (LSTM) for prediction and diagnosis of respiratory diseases. We collected the medical records of inpatients in the respiratory department, including: chief complaint, history of present illness, and chest computed tomography. Pre-processing of clinical records with "jieba" word segmentation module, and the Bidirectional Encoder Representation from Transformers (BERT) model was used to perform word vectorization on the text. The partial and total information of the fused feature set was encoded by convolutional layers, while LSTM layers decoded the encoded information. Results: The precisions of traditional machine-learning, deep-learning methods and our proposed method were 0.6, 0.81, 0.89, and F1 scores were 0.6, 0.81, 0.88, respectively. Conclusion: Compared with traditional machine learning and deep-learning methods that our proposed method had a significantly higher performance, and provided precise identification of respiratory disease.

Catalytical feature of optical nanoprobes of boron nitride quantum dots in the presence of Cu2+ for the determination of dopamine.

Monitoring the concentration of dopamine (DA) is vital for preventing and diagnosing DA related diseases. In contrast to the traditional sensing methods for DA, in which direct or indirect effects on the optical probes are often recorded, a novel sensing concept is disclosed based on as a result of the in situ formation of polydopamine (PDA) originating from the synergetic effect between boron nitride quantum dots (BNQDs) and Cu2+. In the co-presence of BNQDs and Cu2+, DA was catalytically oxidized to PDA, accompanied by an obvious color change from colorless to brown. In contrast to previous reports, in which BNQDs have been employed as an optical probe, herein, the BNQDs not only acted as the optical energy donor, but also as the catalysts for the formation of PDA. The quenching efficiency resulting from the inner filter effect and the electron transfer between the BNQDs and PDA was directly proportional to the concentration of DA, ranging linearly from 2 to 80 µM with a limit of detection of 0.49 µM. The present system exhibited an outstanding selectivity for DA among other interfering coexisting biomolecules. Furthermore, the practical application of the proposed platform was verified by assaying DA in human plasma samples, and satisfactory recoveries ranging from 101.24% to 111.98% were obtained. With the satisfactory reliability, repeatability and stability, the proposed simple sensor showed significant potential for use in DA detection in other biomedical applications.

CRISPR/Cas9-Engineered Universal CD19/CD22 Dual-Targeted CAR-T Cell Therapy for Relapsed/Refractory B-cell Acute Lymphoblastic Leukemia.

PURPOSE: Autologous chimeric antigen receptor T (CAR-T) cell therapy is an effective treatment for relapsed/refractory acute lymphoblastic leukemia (r/r ALL). However, certain characteristics of autologous CAR-T cells can delay treatment availability. Relapse caused by antigen escape after single-targeted CAR-T therapy is another issue. Therefore, we aim to develop CRISPR-edited universal off-the-shelf CD19/CD22 dual-targeted CAR-T cells as a novel therapy for r/r ALL. PATIENTS AND METHODS: In this open-label dose-escalation phase I study, universal CD19/CD22-targeting CAR-T cells (CTA101) with a CRISPR/Cas9-disrupted TRAC region and CD52 gene to avoid host immune-mediated rejection were infused in patients with r/r ALL. Safety, efficacy, and CTA101 cellular kinetics were evaluated. RESULTS: CRISPR/Cas9 technology mediated highly efficient, high-fidelity gene editing and production of universal CAR-T cells. No gene editing-associated genotoxicity or chromosomal translocation was observed. Six patients received CTA101 infusions at doses of 1 (3 patients) and 3 (3 patients) × 106 CAR+ T cells/kg body weight. Cytokine release syndrome occurred in all patients. No dose-limiting toxicity, GvHD, neurotoxicity, or genome editing-associated adverse events have occurred to date. The complete remission (CR) rate was 83.3% on day 28 after CTA101 infusion. With a median follow-up of 4.3 months, 3 of the 5 patients who achieved CR or CR with incomplete hematologic recovery (CR/CRi) remained minimal residual disease (MRD) negative. CONCLUSIONS: CRISPR/Cas9-engineered universal CD19/CD22 CAR-T cells exhibited a manageable safety profile and prominent antileukemia activity. Universal dual-targeted CAR-T cell therapy may offer an alternative therapy for patients with r/r ALL.

Regulating Catalytic Activity of DNA-Templated Silver Nanoclusters Based on their Differential Interactions with DNA Structures and Stimuli-Responsive Structural Transition.

This work reports exquisite engineering of catalytic activity of DNA-templated silver nanoclusters (DNA-AgNCs) based on unique adsorption phenomena of DNAs on DNA-AgNCs and reversible transition between double and triple-stranded DNAs. Four DNA homopolymers exhibit different inhibition effects on the catalytic activity of DNA-AgNCs, poly adenine (polyA) > poly guanine (polyG) > poly cytosine (polyC) > poly thymine (polyT), demonstrating that polyA strands have the strongest adsorption affinity on DNA-AgNCs. Through the formation of T-A•T triplex DNAs, catalytic activity of DNA-AgNCs is restored from the deactivated state by double or single-stranded DNAs, indicating the participation of N7 groups of adenine bases in binding to DNA-AgNCs and blocking active sites. Accordingly, reversibly regulating catalytic activity of DNA-AgNCs can be realized based on DNA input-stimulated transition between duplex and triplex structures. In the end, two low-cost and facile biosensing methods are presented, which are derived from the activity-switchable platform. It is worthy to anticipate that the DNA-AgNCs with controlled catalytic activity will inspire researchers to devise more functionalized nanocatalysts and contribute to the exploration of intelligent biomedicine in the future.