Structural basis for recruitment of peptidoglycan endopeptidase MepS by lipoprotein NlpI.

Peptidoglycan (PG) sacculi surround the cytoplasmic membrane, maintaining cell integrity by withstanding internal turgor pressure. During cell growth, PG endopeptidases cleave the crosslinks of the fully closed sacculi, allowing for the incorporation of new glycan strands and expansion of the peptidoglycan mesh. Outer-membrane-anchored NlpI associates with hydrolases and synthases near PG synthesis complexes, facilitating spatially close PG hydrolysis. Here, we present the structure of adaptor NlpI in complex with the endopeptidase MepS, revealing atomic details of how NlpI recruits multiple MepS molecules and subsequently influences PG expansion. NlpI binding elicits a disorder-to-order transition in the intrinsically disordered N-terminal of MepS, concomitantly promoting the dimerization of monomeric MepS. This results in the alignment of two asymmetric MepS dimers respectively located on the two opposite sides of the dimerization interface of NlpI, thus enhancing MepS activity in PG hydrolysis. Notably, the protein level of MepS is primarily modulated by the tail-specific protease Prc, which is known to interact with NlpI. The structure of the Prc-NlpI-MepS complex demonstrates that NlpI brings together MepS and Prc, leading to the efficient MepS degradation by Prc. Collectively, our results provide structural insights into the NlpI-enabled avidity effect of cellular endopeptidases and NlpI-directed MepS degradation by Prc.

Intrauterine Infusion and Hysteroscopic Injection of Autologous Platelet-Rich Plasma for Patients with a Persistent Thin Endometrium: A Prospective Case-Control Study.

Objectives: To evaluate the effect of intrauterine infusion and hysteroscopic injection of autologous platelet-rich plasma (PRP) in patients with a persistent thin endometrium (EM) undergoing euploid frozen embryo transfer (EFET) cycles. Methods: This prospective case-control study enrolled 116 infertile women with thin EM (<7 mm) who underwent hormone replacement therapy (HRT) for EFET. These women had experienced at least one previous unsuccessful EFET cycle, which either resulted in the cancellation of the cycle or failure of pregnancy. A total of 55 women received an intrauterine infusion of PRP before FET, 38 received a hysteroscopic injection of PRP, and 23 received standard HRT treatment without PRP (control group). Only euploid embryos were transferred in these cycles. The primary outcomes were the implantation rate (IR) and clinical pregnancy rate (CPR) after EFET. Results: After receiving intrauterine infusion and hysteroscopic injection of PRP, 78.2% and 55.3% of patients, respectively, showed an EM thickness exceeding 7 mm, followed by embryo transfer. The hysteroscopic injection group demonstrated significantly higher IR (52%), a higher trend of CPR (52%), and a higher live birth rate (38%) than the control group (18%, 22%, and 4%). Conclusions: Intrauterine infusion and hysteroscopic injection of autologous PRP may be effective methods to increase EM thickness in HRT cycles. According to our results, both methods could increase EM thickness, while hysteroscopic injection appeared to provide more significant assistance in increasing IR, CPR, and live birth rate after EFET in patients with persistent thin EM.

Deep learning-based glomerulus detection and classification with generative morphology augmentation in renal pathology images.

Glomerulus morphology on renal pathology images provides valuable diagnosis and outcome prediction information. To provide better care, an efficient, standardized, and scalable method is urgently needed to optimize the time-consuming and labor-intensive interpretation process by renal pathologists. This paper proposes a deep convolutional neural network (CNN)-based approach to automatically detect and classify glomeruli with different stains in renal pathology images. In the glomerulus detection stage, this paper proposes a flattened Xception with a feature pyramid network (FX-FPN). The FX-FPN is employed as a backbone in the framework of faster region-based CNN to improve glomerulus detection performance. In the classification stage, this paper considers classifications of five glomerulus morphologies using a flattened Xception classifier. To endow the classifier with higher discriminability, this paper proposes a generative data augmentation approach for patch-based glomerulus morphology augmentation. New glomerulus patches of different morphologies are generated for data augmentation through the cycle-consistent generative adversarial network (CycleGAN). The single detection model shows the F1 score up to 0.9524 in H&E and PAS stains. The classification result shows that the average sensitivity and specificity are 0.7077 and 0.9316, respectively, by using the flattened Xception with the original training data. The sensitivity and specificity increase to 0.7623 and 0.9443, respectively, by using the generative data augmentation. Comparisons with different deep CNN models show the effectiveness and superiority of the proposed approach.

Incidence trends for common subtypes of T-cell lymphoma in Taiwan and the United States from 2008-2020.

The incidence of T-cell lymphoma (TCL) has been continually increasing in Taiwan and the United States (US) in recent years. This epidemiological study using population-based registry data aimed to determine the incidence patterns of common subtypes of TCL in Taiwan from 2008-2020 and compare them with those in the US and the Asian/Pacific Islander (API) population. Subtypes included angioimmunoblastic T-cell lymphoma (AITL); extranodal NK/T-cell lymphoma, nasal or other type (ENKTL); peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS); and anaplastic large cell lymphoma (ALCL). The total number of patients newly diagnosed with TCL during 2008-2020 was 4477, 3171, and 48,889 in Taiwan, API, and the US, respectively. Except the incidence rate of AITL in Taiwan, the incidence rates of these common TCL subtypes showed downward trends in all studied populations. There was also a significant increase in the relative frequency of AITL among TCL in Taiwan, with an annual percent change of 4.44 (p < 0.001), from 8.44% in 2002 to 20.63% in 2020. The rapid development of diagnostics may be the main factor contributing to this rise in incidence.

Adaptation in Unstable Environments and Global Gene Losses: Small but Stable Gene Networks by the May-Wigner Theory.

Although gene loss is common in evolution, it remains unclear whether it is an adaptive process. In a survey of seven major mangrove clades that are woody plants in the intertidal zones of daily environmental perturbations, we noticed that they generally evolved reduced gene numbers. We then focused on the largest clade of Rhizophoreae and observed the continual gene set reduction in each of the eight species. A great majority of gene losses are concentrated on environmental interaction processes, presumably to cope with the constant fluctuations in the tidal environments. Genes of the general processes for woody plants are largely retained. In particular, fewer gene losses are found in physiological traits such as viviparous seeds, high salinity, and high tannin content. Given the broad and continual genome reductions, we propose the May-Wigner theory (MWT) of system stability as a possible mechanism. In MWT, the most effective solution for buffering continual perturbations is to reduce the size of the system (or to weaken the total genic interactions). Mangroves are unique as immovable inhabitants of the compound environments in the land-sea interface, where environmental gradients (such as salinity) fluctuate constantly, often drastically. Extending MWT to gene regulatory network (GRN), computer simulations and transcriptome analyses support the stabilizing effects of smaller gene sets in mangroves vis-à-vis inland plants. In summary, we show the adaptive significance of gene losses in mangrove plants, including the specific role of promoting phenotype innovation and a general role in stabilizing GRN in unstable environments as predicted by MWT.

Wolfberry genome database: integrated genomic datasets for studying molecular biology.

Wolfberry, also known as goji berry or Lycium barbarum, is a highly valued fruit with significant health benefits and nutritional value. For more efficient and comprehensive usage of published L. barbarum genomic data, we established the Wolfberry database. The utility of the Wolfberry Genome Database (WGDB) is highlighted through the Genome browser, which enables the user to explore the L. barbarum genome, browse specific chromosomes, and access gene sequences. Gene annotation features provide comprehensive information about gene functions, locations, expression profiles, pathway involvement, protein domains, and regulatory transcription factors. The transcriptome feature allows the user to explore gene expression patterns using transcripts per kilobase million (TPM) and fragments per kilobase per million mapped reads (FPKM) metrics. The Metabolism pathway page provides insights into metabolic pathways and the involvement of the selected genes. In addition to the database content, we also introduce six analysis tools developed for the WGDB. These tools offer functionalities for gene function prediction, nucleotide and amino acid BLAST analysis, protein domain analysis, GO annotation, and gene expression pattern analysis. The WGDB is freely accessible at https://cosbi7.ee.ncku.edu.tw/Wolfberry/. Overall, WGDB serves as a valuable resource for researchers interested in the genomics and transcriptomics of L. barbarum. Its user-friendly web interface and comprehensive data facilitate the exploration of gene functions, regulatory mechanisms, and metabolic pathways, ultimately contributing to a deeper understanding of wolfberry and its potential applications in agronomy and nutrition.

Gas exchange and chlorophyll fluorescence responses of Camellia sinensis grown under various cultivations in different seasons.

Sod culture (SC) and conventional agriculture (CA) represent two distinct field management approaches utilized in the cultivation of tea plants in Taiwan. In this study, we employed gas exchange and chlorophyll fluorescence techniques to assess the impact of SC and CA methods on the photosynthetic machinery of Camellia sinensis cv. TTES No.12 (Jhinhsuan) in response to variable light intensities across different seasons. In spring, at photosynthetic photon flux densities (PPFD) ranging from 800 to 2,000 µmol photon m-2 s-1, the net photosynthesis rate (Pn, 10.43 µmol CO2 m-2 s-1), stomatal conductance (Gs, 126.11 mmol H2O m-2 s-1), electron transport rate (ETR, 137.94), and ΔF/Fm' and Fv/Fm (50.37) values for plants grown using SC were comparatively higher than those cultivated under CA. Conversely, the non-photochemical quenching (NPQ) values for SC-grown plants were relatively lower (3.11) compared to those grown under CA at 800 to 2,000 PPFD in spring. Additionally, when tea plants were exposed to PPFD levels below 1,500 µmol photon m- 2 s- 1, there was a concurrent increase in Pn, Gs, ETR, and NPQ. These photosynthetic parameters are crucial for devising models that optimize cultivation practices across varying seasons and specific tillage requirements, and for predicting photosynthetic and respiratory responses of tea plants to seasonally or artificially altered light irradiances. The observed positive impacts of SC on maximum photosynthetic rate (Amax), Fv/Fm, Gs, water-use efficiency (WUE), and ETR suggest that SC is advantageous for enhancing the productivity of tea plants, thereby offering a more adaptable management model for tea gardens.

Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans.

Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approaches. Deep learning models (DLMs) have shown great promise in enhancing various aspects of clinical practice. There is limited literature available on the use of DLMs specifically for trauma image evaluation. In this study, we developed a DLM aimed at detecting solid organ injuries to assist medical professionals in rapidly identifying life-threatening injuries. The study enrolled patients from a single trauma center who received abdominal CT scans between 2008 and 2017. Patients with spleen, liver, or kidney injury were categorized as the solid organ injury group, while others were considered negative cases. Only images acquired from the trauma center were enrolled. A subset of images acquired in the last year was designated as the test set, and the remaining images were utilized to train and validate the detection models. The performance of each model was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value based on the best Youden index operating point. The study developed the models using 1302 (87%) scans for training and tested them on 194 (13%) scans. The spleen injury model demonstrated an accuracy of 0.938 and a specificity of 0.952. The accuracy and specificity of the liver injury model were reported as 0.820 and 0.847, respectively. The kidney injury model showed an accuracy of 0.959 and a specificity of 0.989. We developed a DLM that can automate the detection of solid organ injuries by abdominal CT scans with acceptable diagnostic accuracy. It cannot replace the role of clinicians, but we can expect it to be a potential tool to accelerate the process of therapeutic decisions for trauma care.

Predicting long-term time to cardiovascular incidents using myocardial perfusion imaging and deep convolutional neural networks.

Myocardial perfusion imaging (MPI) is a clinical tool which can assess the heart's perfusion status, thereby revealing impairments in patients' cardiac function. Within the MPI modality, the acquired three-dimensional signals are typically represented as a sequence of two-dimensional grayscale tomographic images. Here, we proposed an end-to-end survival training approach for processing gray-scale MPI tomograms to generate a risk score which reflects subsequent time to cardiovascular incidents, including cardiovascular death, non-fatal myocardial infarction, and non-fatal ischemic stroke (collectively known as Major Adverse Cardiovascular Events; MACE) as well as Congestive Heart Failure (CHF). We recruited a total of 1928 patients who had undergone MPI followed by coronary interventions. Among them, 80% (n = 1540) were randomly reserved for the training and 5- fold cross-validation stage, while 20% (n = 388) were set aside for the testing stage. The end-to-end survival training can converge well in generating effective AI models via the fivefold cross-validation approach with 1540 patients. When a candidate model is evaluated using independent images, the model can stratify patients into below-median-risk (n = 194) and above-median-risk (n = 194) groups, the corresponding survival curves of the two groups have significant difference (P < 0.0001). We further stratify the above-median-risk group to the quartile 3 and 4 group (n = 97 each), and the three patient strata, referred to as the high, intermediate and low risk groups respectively, manifest statistically significant difference. Notably, the 5-year cardiovascular incident rate is less than 5% in the low-risk group (accounting for 50% of all patients), while the rate is nearly 40% in the high-risk group (accounting for 25% of all patients). Evaluation of patient subgroups revealed stronger effect size in patients with three blocked arteries (Hazard ratio [HR]: 18.377, 95% CI 3.719-90.801, p < 0.001), followed by those with two blocked vessels at HR 7.484 (95% CI 1.858-30.150; p = 0.005). Regarding stent placement, patients with a single stent displayed a HR of 4.410 (95% CI 1.399-13.904; p = 0.011). Patients with two stents show a HR of 10.699 (95% CI 2.262-50.601; p = 0.003), escalating notably to a HR of 57.446 (95% CI 1.922-1717.207; p = 0.019) for patients with three or more stents, indicating a substantial relationship between the disease severity and the predictive capability of the AI for subsequent cardiovascular inciidents. The success of the MPI AI model in stratifying patients into subgroups with distinct time-to-cardiovascular incidents demonstrated the feasibility of proposed end-to-end survival training approach.

Genomic evidence for rediploidization and adaptive evolution following the whole-genome triplication.

Whole-genome duplication (WGD), or polyploidy, events are widespread and significant in the evolutionary history of angiosperms. However, empirical evidence for rediploidization, the major process where polyploids give rise to diploid descendants, is still lacking at the genomic level. Here we present chromosome-scale genomes of the mangrove tree Sonneratia alba and the related inland plant Lagerstroemia speciosa. Their common ancestor has experienced a whole-genome triplication (WGT) approximately 64 million years ago coinciding with a period of dramatic global climate change. Sonneratia, adapting mangrove habitats, experienced extensive chromosome rearrangements post-WGT. We observe the WGT retentions display sequence and expression divergence, suggesting potential neo- and sub-functionalization. Strong selection acting on three-copy retentions indicates adaptive value in response to new environments. To elucidate the role of ploidy changes in genome evolution, we improve a model of the polyploidization-rediploidization process based on genomic evidence, contributing to the understanding of adaptive evolution during climate change.

Associations between the artificial intelligence scoring system and live birth outcomes in preimplantation genetic testing for aneuploidy cycles.

BACKGROUND: Several studies have demonstrated that iDAScore is more accurate in predicting pregnancy outcomes in cycles without preimplantation genetic testing for aneuploidy (PGT-A) compared to KIDScore and the Gardner criteria. However, the effectiveness of iDAScore in cycles with PGT-A has not been thoroughly investigated. Therefore, this study aims to assess the association between artificial intelligence (AI)-based iDAScore (version 1.0) and pregnancy outcomes in single-embryo transfer (SET) cycles with PGT-A. METHODS: This retrospective study was approved by the Institutional Review Board of Chung Sun Medical University, Taichung, Taiwan. Patients undergoing SET cycles (n = 482) following PGT-A at a single reproductive center between January 2017 and June 2021. The blastocyst morphology and morphokinetics of all embryos were evaluated using a time-lapse system. The blastocysts were ranked based on the scores generated by iDAScore, which were defined as AI scores, or by KIDScore D5 (version 3.2) following the manufacturer's protocols. A single blastocyst without aneuploidy was transferred after examining the embryonic ploidy status using a next-generation sequencing-based PGT-A platform. Logistic regression analysis with generalized estimating equations was conducted to assess whether AI scores are associated with the probability of live birth (LB) while considering confounding factors. RESULTS: Logistic regression analysis revealed that AI score was significantly associated with LB probability (adjusted odds ratio [OR] = 2.037, 95% confidence interval [CI]: 1.632-2.542) when pulsatility index (PI) level and types of chromosomal abnormalities were controlled. Blastocysts were divided into quartiles in accordance with their AI score (group 1: 3.0-7.8; group 2: 7.9-8.6; group 3: 8.7-8.9; and group 4: 9.0-9.5). Group 1 had a lower LB rate (34.6% vs. 59.8-72.3%) and a higher rate of pregnancy loss (26% vs. 4.7-8.9%) compared with the other groups (p < 0.05). The receiver operating characteristic curve analysis verified that the iDAScore had a significant but limited ability to predict LB (area under the curve [AUC] = 0.64); this ability was significantly weaker than that of the combination of iDAScore, type of chromosomal abnormalities, and PI level (AUC = 0.67). In the comparison of the LB groups with the non-LB groups, the AI scores were significantly lower in the non-LB groups, both for euploid (median: 8.6 vs. 8.8) and mosaic (median: 8.0 vs. 8.6) SETs. CONCLUSIONS: Although its predictive ability can be further enhanced, the AI score was significantly associated with LB probability in SET cycles. Euploid or mosaic blastocysts with low AI scores (≤ 7.8) were associated with a lower LB rate, indicating the potential of this annotation-free AI system as a decision-support tool for deselecting embryos with poor pregnancy outcomes following PGT-A.

A computed tomography radiomics-based model for predicting osteoporosis after breast cancer treatment.

Many treatments against breast cancer decrease the level of estrogen in blood, resulting in bone loss, osteoporosis and fragility fractures in breast cancer patients. This retrospective study aimed to evaluate a novel opportunistic screening for cancer treatment-induced bone loss (CTIBL) in breast cancer patients using CT radiomics. Between 2011 and 2021, a total of 412 female breast cancer patients who received treatment and were followed up in our institution, had post-treatment dual-energy X-ray absorptiometry (DXA) examination of the lumbar vertebrae and had post-treatment chest CT scan that encompassed the L1 vertebra, were included in this study. Results indicated that the T-score of L1 vertebra had a strongly positive correlation with the average T-score of L1-L4 vertebrae derived from DXA (r = 0.91, p < 0.05). On multivariable analysis, four clinical variables (age, body weight, menopause status, aromatase inhibitor exposure duration) and three radiomic features extracted from the region of interest of L1 vertebra (original_firstorder_RootMeanSquared, wavelet.HH_glcm_InverseVariance, and wavelet.LL_glcm_MCC) were selected for building predictive models of L1 T-score and bone health. The predictive model combining clinical and radiomic features showed the greatest adjusted R2 value (0.557), sensitivity (83.6%), specificity (74.2%) and total accuracy (79.4%) compared to models that relied solely on clinical data, radiomic features, or Hounsfield units. In conclusion, the clinical-radiomic predictive model may be used as an opportunistic screening tool for early identification of breast cancer survivors at high risk of CTIBL based on non-contrast CT images of the L1 vertebra, thereby facilitating early intervention for osteoporosis.

Photo-protection and photo-inhibition during light induction in Barbula indica and Conocephalum conicum under different light gradients.

The objectives of this study were to measure the chlorophyll fluorescence (ChlF) parameters of Barbula indica (Hook.) Spreng and Conocephalum conicum (L.) Dumort subjected to various light intensities (LI) as a reflection of their adaptability to their habitats. The electron transport rate (ETR) of all plants under 500 µmol m-2 s-1 photosynthetic photon flux density (PPFD) was significantly higher than other LI treatments, implying that these plants could be grown under a specific and optimal light intensity adapted to 500 PPFD conditions. As LI increased from 50 to 2,000 PPFD, we observed in all plants increased non-photochemical quenching (NPQ) and photo-inhibitory quenching (qI) and decreased photosystem II efficiency (ΦPSII), potential quantum efficiency of PSII (Fv/Fm), actual PSII efficiency (ΔF/Fm'%), and Fv/Fm%. In addition, energy-dependent quenching (qE), the light protection system (qE + qZ + qT), and qI increased as ΦPSII decreased and photo-inhibition% increased under 1000, 1500, and 2000 PPFD conditions, suggesting that these plants had higher photo-protective ability under high LI treatments to maintain higher photosynthetic system performance. B. indica plants remained photochemically active and maintained higher qE under 300, 500, and 1000 PPFD, whereas C. conicum qZ + qT exhibited higher photo-protection under 500, 1000, and 1500 PPFD conditions. These ChlF indices can be used for predicting photosynthetic responses to light induction in different bryophytes and provide a theoretical basis for ecological monitoring.

Effect of intraoperative hand-grip position on surgical outcome of thumb carpometacarpal arthrodesis.

BACKGROUND: A variety of surgical techniques had been developed over the past few decades for treating thumb carpometacarpal joint (CMCJ) osteoarthritis (OA). However, there are currently no accepted consensus on the ideal treatment for thumb CMCJ OA. Arthrodesis was one of the widely popular treatment methods; however, studies have showed that non-union rates were as high as 50%, with higher complications such as osteoarthritis of neighbouring joints and higher revision surgeries required as compared to other surgical methods. Patients with arthrodesis were also reported to have decreased thumb range of motion and loss of opponens function. Currently, there are numerous intraoperative positioning techniques for arthrodesis which could be confusing for young surgeons. With recent developments of fixation plates and better understanding of the wrist anatomy, this retrospective review aimed to evaluate the efficacy of our intraoperative hand-grip positioning method for arthrodesis of thumb CMCJ OA. What are the postoperative functional outcomes of (1) T-hook plates and (2) our intraoperative hand-grip positioning method for Eaton III thumb CMCJ OA arthrodesis by evaluating pain visual analogue scale (VAS) score, Disabilities of the Arm, Shoulder and Hand questionnaires (DASH), Mayo Wrist scores, capability of thumb opposition (Kapandji score), and comparing pre- and postoperative grip and pinch strength? METHODS: Twenty patients with CMCJ OA underwent arthrodesis using our intraoperative hand-grip positioning method and T-hook plates and screws (Acumed, USA). Patients were evaluated preoperatively and at 1, 3, 6 and 12 months postoperatively. Radiologic assessment including fusion evaluation, evaluation of radial and palmar abduction angles was done on hand X-rays. RESULTS: Twenty patients with a minimum follow-up duration of 12 months were included in this study. 100% fusion rate was achieved with only 1 case of complication involving radial sensory nerve neuropathy which was resolved after removal of implant and neurolysis. Significant improvement in pain and Mayo Wrist scores were noted 3 months postoperatively, whilst DASH score exhibited significant improvements after 6 months of follow-up (p < 0.05). Even though there were no significant differences observed between preoperative and postoperative grip strength, pinch strength and Kapandji scores, positive recovery trends were noted for all parameters with these functions surpassing preoperative levels after 12 months of follow-up. Significant improvements on hand X-rays were also noted for both postoperative radial and palmar abduction angles. CONCLUSIONS: There is currently no consensus on the ideal treatment method for thumb CMCJ OA. In this study, we would like to propose a simple intraoperative hand-grip positioning method with T-hook plates for arthrodesis. As seen from our results, our technique was able to provide satisfactory and replicable postoperative results and thus we would like to propose our hand-grip positioning method with T-hook plates fixation for subsequent treatment of patients with Eaton stage III thumb CMCJ OA.

Results and Complications After Single-Stage Repair of Aortopulmonary Window and Interrupted Aortic Arch in a 32-Week Preterm and a Full-Term Neonate.

BACKGROUND An aortopulmonary window (APW) is an uncommon congenital defect of the septation between the ascending aorta and pulmonary trunk. The combination of APW and interrupted aortic arch (IAA) is even rarer, with the hallmark characteristics of high peri-operative mortality and postoperative obstruction of the aortic arch, pulmonary artery, and left main bronchus. These complications often need re-interventions. CASE REPORT We present 2 cases with diagnoses of APW and IAA that were treated with single-stage repair. Case 1: A male 32-week premature newborn (weight 1789 g) had APW type I and IAA type A. He had severe postoperative aortic arch obstruction on postoperative day 1, and we re-intervened promptly. He was still asymptomatic after 6 years. Case 2: A male term neonate had APW type III and IAA type A. He had left vocal cord paralysis and left bronchial compression postoperatively. We applied prolonged noninvasive respiratory supports. The complications resolved without re-intervention on postoperative day 66. Progressive arch stenosis at anastomosis after operation required close follow-up with echocardiography. CONCLUSIONS These 2 reports highlight the feasibility of single-stage surgical repair while addressing 2 challenges: (1) Recurrent arch stenosis: Lower body weight and direct end-to-side anastomosis without patch augmentation could be risk factors for re-intervention. (2) Bronchial compression: Presentation of the second reported case implied that bronchial compression may not warrant immediate re-intervention unless there is complete obstruction, persistent atelectasis, or recurrent infection. Further studies on long-term outcomes of different surgical procedure would help us to clarify the proper way to avoid re-intervention.

Monoclonal enolase-1 blocking antibody ameliorates pulmonary inflammation and fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic fatal disease with limited therapeutic options. The infiltration of monocytes and fibroblasts into the injured lungs is implicated in IPF. Enolase-1 (ENO1) is a cytosolic glycolytic enzyme which could translocate onto the cell surface and act as a plasminogen receptor to facilitate cell migration via plasmin activation. Our proprietary ENO1 antibody, HL217, was screened for its specific binding to ENO1 and significant inhibition of cell migration and plasmin activation (patent: US9382331B2). METHODS: In this study, effects of HL217 were evaluated in vivo and in vitro for treating lung fibrosis. RESULTS: Elevated ENO1 expression was found in fibrotic lungs in human and in bleomycin-treated mice. In the mouse model, HL217 reduced bleomycin-induced lung fibrosis, inflammation, body weight loss, lung weight gain, TGF-ß upregulation in bronchial alveolar lavage fluid (BALF), and collagen deposition in lung. Moreover, HL217 reduced the migration of peripheral blood mononuclear cells (PBMC) and the recruitment of myeloid cells into the lungs. In vitro, HL217 significantly reduced cell-associated plasmin activation and cytokines secretion from primary human PBMC and endothelial cells. In primary human lung fibroblasts, HL217 also reduced cell migration and collagen secretion. CONCLUSIONS: These findings suggest multi-faceted roles of cell surface ENO1 and a potential therapeutic approach for pulmonary fibrosis.

Deep neural network based tissue deconvolution of circulating tumor cell RNA.

Prior research has shown that the deconvolution of cell-free RNA can uncover the tissue origin. The conventional deconvolution approaches rely on constructing a reference tissue-specific gene panel, which cannot capture the inherent variation present in actual data. To address this, we have developed a novel method that utilizes a neural network framework to leverage the entire training dataset. Our approach involved training a model that incorporated 15 distinct tissue types. Through one semi-independent and two complete independent validations, including deconvolution using a semi in silico dataset, deconvolution with a custom normal tissue mixture RNA-seq data, and deconvolution of longitudinal circulating tumor cell RNA-seq (ctcRNA) data from a cancer patient with metastatic tumors, we demonstrate the efficacy and advantages of the deep-learning approach which were exerted by effectively capturing the inherent variability present in the dataset, thus leading to enhanced accuracy. Sensitivity analyses reveal that neural network models are less susceptible to the presence of missing data, making them more suitable for real-world applications. Moreover, by leveraging the concept of organotropism, we applied our approach to trace the migration of circulating tumor cell-derived RNA (ctcRNA) in a cancer patient with metastatic tumors, thereby highlighting the potential clinical significance of early detection of cancer metastasis.

A 5+1 assemble-to-activate mechanism of the Lon proteolytic machine.

Many AAA+ (ATPases associated with diverse cellular activities) proteins function as protein or DNA remodelers by threading the substrate through the central pore of their hexameric assemblies. In this ATP-dependent translocating state, the substrate is gripped by the pore loops of the ATPase domains arranged in a universal right-handed spiral staircase organization. However, the process by which a AAA+ protein is activated to adopt this substrate-pore-loop arrangement remains unknown. We show here, using cryo-electron microscopy (cryo-EM), that the activation process of the Lon AAA+ protease may involve a pentameric assembly and a substrate-dependent incorporation of the sixth protomer to form the substrate-pore-loop contacts seen in the translocating state. Based on the structural results, we design truncated monomeric mutants that inhibit Lon activity by binding to the native pentamer and demonstrated that expressing these monomeric mutants in Escherichia coli cells containing functional Lon elicits specific phenotypes associated with lon deficiency, including the inhibition of persister cell formation. These findings uncover a substrate-dependent assembly process for the activation of a AAA+ protein and demonstrate a targeted approach to selectively inhibit its function within cells.

Unrevealed roles of extracellular enolase­1 (ENO1) in promoting glycolysis and pro­cancer activities in multiple myeloma via hypoxia­inducible factor 1α.

The involvement of enolase­1 (ENO1), intracellularly or extracellularly, has been implicated in cancer development. Moreover, anticancer activities of an ENO1­targeting antibody has demonstrated the pathological roles of extracellular ENO1 (surface or secreted forms). However, although ENO1 was first identified as a glycolytic enzyme in the cytosol, to the best of our knowledge, extracellular ENO1 has not been implicated in glycolysis thus far. In the present study, the effects of extracellular ENO1 on glycolysis and other related pro­cancer activities were investigated in multiple myeloma (MM) cells in vitro and in vivo. Knockdown of ENO1 expression reduced lactate production, cell viability, cell migration and surface ENO1 expression in MM cells. Notably, addition of extracellular ENO1 protein in cancer cell culture enhanced glycolytic activity, hypoxia­inducible factor 1­α (HIF­1α) expression, glycolysis­related gene (GRG) expression and pro­cancer activities, such as cell migration, cell viability and tumor­promoting cytokine secretion. Consistently, these extracellular ENO1­induced cellular effects were inhibited by an ENO1­specific monoclonal antibody (mAb). In addition, extracellular ENO1­mediated glycolysis, GRG expression and pro­cancer activities were also reduced by HIF­1α silencing. Lastly, administration of an ENO1 mAb reduced tumor growth and serum lactate levels in an MM xenograft model. These results suggested that extracellular ENO1 (surface or secreted forms) enhanced a HIF­1α­mediated glycolytic pathway, in addition to its already identified roles. Therefore, the results of the present study highlighted the therapeutic potential of ENO1­specific antibodies in treating MM, possibly via glycolysis inhibition, and warrant further studies in other types of cancer.

Structural basis for the hydrolytic activity of the transpeptidase-like protein DpaA to detach Braun's lipoprotein from peptidoglycan.

IMPORTANCE: Cross-linking reaction of Braun's lipoprotein (Lpp) to peptidoglycan (PG) is catalyzed by some members of the YkuD family of transpeptidases. However, the exact opposite reaction of cleaving the Lpp-PG cross-link is performed by DpaA, which is also a YkuD-like protein. In this work, we determined the crystal structure of DpaA to provide the molecular rationale for the ability of the transpeptidase-like protein to cleave, rather than form, the Lpp-PG linkage. Our findings also revealed the structural features that distinguish the different functional types of the YkuD family enzymes from one another.