Reduced-dose chemotherapy and blinatumomab as induction treatment for newly diagnosed Ph-negative B-cell precursor acute lymphoblastic leukemia: a phase 2 trial.

Blinatumomab has emerged as a promising component of first-line therapy for acute B-cell precursor lymphoblastic leukemia (BCP-ALL), bolstering treatment efficacy. To mitigate CD19 selection pressure and reduce the incidence of blinatumomab-associated toxicities, pre-treatment chemotherapy is recommended before administering blinatumomab. From September 2022 to December 2023, we conducted a single-arm, multicenter, phase 2 trial (NCT05557110) in newly diagnosed Philadelphia chromosome-negative BCP-ALL (Ph-negative BCP-ALL) patients. Participants received induction treatment with reduced-dose chemotherapy (RDC), comprising idarubicin, vindesine, and dexamethasone over 7 days, followed by 2 weeks of blinatumomab. Those failing to achieve composite complete remission (CRc) received an additional 2 weeks of blinatumomab. The primary endpoint was the CRc rate post initial induction treatment. Of the 35 enrolled patients, 33 (94%) achieved CRc after 2 weeks of blinatumomab, with 30 (86%) achieving measurable residual disease (MRD) negativity. Two patients extended blinatumomab to 4 weeks. With either 2 or 4 weeks of blinatumomab treatment, all patients achieved CR (35/35) and 89% (31/35) were MRD negativity. The median time to CR was 22 days. Immune effector cell-associated neurotoxicity syndrome was limited (14%, all grade 1). Non-hematological adverse events of grade 3 or higher included pneumonia (17%), sepsis (6%), and cytokine release syndrome (9%). With a median follow-up of 11.5 months, estimated 1-year overall survival and 1-year progression-free survival rates were 97.1% and 82.2%, respectively. These findings affirm that RDC followed by blinatumomab is an effective and well-tolerated induction regimen for newly diagnosed Ph-negative BCP-ALL, supporting a shift towards less intensive and more targeted therapeutic approaches. Trial registration: https://www.clinicaltrials.Gov . Identifier NCT05557110.

The updated mouse universal genotyping array bioinformatic pipeline improves genetic QC in laboratory mice.

The MiniMUGA genotyping array is a popular tool for genetic quality control of laboratory mice and genotyping samples from most experimental crosses involving laboratory strains, particularly for reduced complexity crosses. The content of the production version of the MiniMUGA array is fixed; however, there is the opportunity to improve the array's performance and the associated report's usefulness by leveraging thousands of samples genotyped since the initial description of MiniMUGA. Here, we report our efforts to update and improve marker annotation, increase the number and the reliability of the consensus genotypes for classical inbred strains and substrains, and increase the number of constructs reliably detected with MiniMUGA. In addition, we have implemented key changes in the informatics pipeline to identify and quantify the contribution of specific genetic backgrounds to the makeup of a given sample, remove arbitrary thresholds, include the Y Chromosome and mitochondrial genome in the ideogram, and improve robust detection of the presence of commercially available substrains based on diagnostic alleles. Finally, we have updated the layout of the report to simplify the interpretation and completeness of the analysis and added a section summarizing the ideogram in table format. These changes will be of general interest to the mouse research community and will be instrumental in our goal of improving the rigor and reproducibility of mouse-based biomedical research.

Early hippocampal high-amplitude rhythmic spikes predict post-traumatic epilepsy in mice.

Oscillations, a highly conserved brain function across mammalian species, play a pivotal role in both brain physiology and pathology. Traumatic brain injury (TBI) frequently results in subacute and chronic alterations in brain oscillations, which are often associated with complications like post-traumatic epilepsy (PTE) in patients and animal models. We recently conducted longitudinal recordings of local field potential from the contralateral hippocampus of 12 strains of recombinant inbred Collaborative Cross (CC) mice and classical laboratory inbred C57BL/6 J mice after lateral fluid percussion injury. In this study, we profiled the acute (<12 h post-injury) and subacute (12-48 h post-injury) hippocampal oscillatory responses to TBI and evaluated their predictive value for PTE. We found dynamic high-amplitude rhythmic spikes with elevated power density and reduced signal complexity that prevailed exclusively during the acute phase in CC031 mice, which later developed PTE. This characteristic early brain oscillatory alteration was absent in CC031 sham controls, as well as in other CC strains and reference C57BL/6 J mice that did not develop PTE after TBI. Our findings offer quantitative measures linking early hippocampal brain oscillation to PTE at a population level in mice. These insights enhance understanding of circuit mechanisms and suggest potential targets for neuromodulatory intervention.

Texture of Hot-Compressed Metastable ß-Titanium Alloy Ti5321 Studied by Neutron Diffraction.

The textures of the ß- and α-phases of the metastable ß-titanium alloy Ti5321 after hot deformation were investigated by neutron diffraction. A hot-rolled bar was solutionized in the ß-phase field and then hot compressed above and below the ß-transus temperature. The initial texture after full recrystallization and grain growth in the ß-phase field exhibits a weak cube component {001}<100> and minor {112}<110> and {111}<110> components. After hot compression, a <100> fiber texture is observed, increasing in intensity with compression temperature. Below the ß-transus temperature, dynamic recrystallization of the ß-phase and dynamic spheroidization of the α-phase interact strongly. The texture of the α-phase is a <11-20> fiber texture, increasing in intensity with decreasing compression temperature. The mechanisms of texture formation during hot compression are discussed.

A fibronectin gradient remodels mixed-phase mesoderm.

Physical processes ultimately shape tissue during development. Two emerging proposals are that cells migrate toward stiffer tissue (durotaxis) and that the extent of cell rearrangements reflects tissue phase, but it is unclear whether and how these concepts are related. Here, we identify fibronectin-dependent tissue stiffness as a control variable that underlies and unifies these phenomena in vivo. In murine limb bud mesoderm, cells are either caged, move directionally, or intercalate as a function of their location along a stiffness gradient. A modified Landau phase equation that incorporates tissue stiffness accurately predicts cell diffusivity upon loss or gain of fibronectin. Fibronectin is regulated by WNT5A-YAP feedback that controls cell movements, tissue shape, and skeletal pattern. The results identify a key determinant of phase transition and show how fibronectin-dependent directional cell movement emerges in a mixed-phase environment in vivo.

Autologous Tissue Repair and Total Face Restoration.

Importance: Total face restoration remains a challenge in modern reconstructive surgery. After 17 years of experiments and preliminary clinical studies, a new concept of face prefabrication was developed for face restoration with autologous tissue. Objective: To evaluate the long-term results of face restoration with autologous tissue and report a finalized and standardized approach of face prefabrication. Design, Setting, and Participants: In this single-center long-term retrospective study, 32 patients who underwent total face restoration between 2005 and 2022 were reviewed. These patients underwent total facial reconstruction, which included flap prefabrication, 3-dimensional printing, tissue expansion, and flap transfer with aid of indocyanine green angiography (IGA). The flap first undergoes prefabrication by transferring vascularized fascia under the skin of the selected chest. A tissue expander is then placed under the fascia to create a large, thin, reliable skin flap after expansion. Once completed, the flap is transferred to the face during the second stage of the reconstruction. Intraoperative IGA is performed to guide the design of subsequent openings for facial fissures. Data were analyzed from July to September 2023. Main Outcomes and Measures: Flap healing, reconstructive outcome, and patient recovery were assessed during follow-up. Three questionnaires, including the 36-Item Short Form Health Survey (SF-36), Aesthetic and Functional Status Score of Facial Soft-Tissue Deformities/Defects, and the EuroQoL Health-Related Quality of Life (EQ-5D-5L), were used to evaluate the quality of life and satisfaction with facial aesthetic and functional status. Results: Of 24 included patients, 14 (58%) were male, and the mean (range) age was 32.9 (8-62) years. The mean (range) follow-up was 5.6 (2-12) years. All patients reported a significant improvement in quality of life (SF-36), especially in mean (SD) social functioning (preoperative score, 53.65 [34.51]; postoperative score, 80.73 [19.10]) and emotional stability (preoperative score, 56.67 [25.55]; postoperative score, 71.17 [18.51]). A total of 22 patients (92%) went back to work. Mean (SD) facial aesthetic status (preoperative score, 4.96 [3.26]; postoperative score, 11.52 [3.49]; P < .001) and functional status (preoperative score, 11.09 [3.51]; postoperative score, 15.78 [3.26]; P < .001) also improved. In addition, there was a significant increase in overall satisfaction and self-reported health status (preoperative score, 8.13 [1.52]; postoperative score, 3.58 [2.31]). Conclusions and Relevance: In this study, 5-year follow-up results suggested that this innovative approach to total face restoration offered a safe and valid option for indicated patients, with acceptable reconstructive and cosmetic outcomes.

Arid1b haploinsufficiency in pyramidal neurons causes cellular and circuit changes in neocortex but is not sufficient to produce behavioral or seizure phenotypes.

Arid1b is a high confidence risk gene for autism spectrum disorder that encodes a subunit of a chromatin remodeling complex expressed in neuronal progenitors. Haploinsufficiency causes a broad range of social, behavioral, and intellectual disability phenotypes, including Coffin-Siris syndrome. Recent work using transgenic mouse models suggests pathology is due to deficits in proliferation, survival, and synaptic development of cortical neurons. However, there is conflicting evidence regarding the relative roles of excitatory projection neurons and inhibitory interneurons in generating abnormal cognitive and behavioral phenotypes. Here, we conditionally knocked out either one or both copies of Arid1b from excitatory projection neuron progenitors and systematically investigated the effects on intrinsic membrane properties, synaptic physiology, social behavior, and seizure susceptibility. We found that disrupting Arid1b expression in excitatory neurons alters their membrane properties, including hyperpolarizing action potential threshold; however, these changes depend on neuronal subtype. Using paired whole-cell recordings, we found increased synaptic connectivity rate between projection neurons. Furthermore, we found reduced strength of excitatory synapses to parvalbumin (PV)-expression inhibitory interneurons. These data suggest an increase in the ratio of excitation to inhibition. However, the strength of inhibitory synapses from PV interneurons to excitatory neurons was enhanced, which may rebalance this ratio. Indeed, Arid1b haploinsufficiency in projection neurons was insufficient to cause social deficits and seizure phenotypes observed in a preclinical germline haploinsufficient mouse model. Our data suggest that while excitatory projection neurons likely contribute to autistic phenotypes, pathology in these cells is not the primary cause.

An Irak1-Mecp2 tandem duplication mouse model for the study of MECP2 duplication syndrome.

MECP2 duplication syndrome (MDS) is a neurodevelopmental disorder caused by tandem duplication of the MECP2 locus and its surrounding genes, including IRAK1. Current MDS mouse models involve transgenic expression of MECP2 only, limiting their applicability to the study of the disease. Herein, we show that an efficient and precise CRISPR/Cas9 fusion proximity-based approach can be utilized to generate an Irak1-Mecp2 tandem duplication mouse model ('Mecp2 Dup'). The Mecp2 Dup mouse model recapitulates the genomic landscape of human MDS by harboring a 160 kb tandem duplication encompassing Mecp2 and Irak1, representing the minimal disease-causing duplication, and the neighboring genes Opn1mw and Tex28. The Mecp2 Dup model exhibits neuro-behavioral abnormalities, and an abnormal immune response to infection not previously observed in other mouse models, possibly owing to Irak1 overexpression. The Mecp2 Dup model thus provides a tool to investigate MDS disease mechanisms and develop potential therapies applicable to patients.

Machine Learning Assisted Electronic/Ionic Skin Recognition of Thermal Stimuli and Mechanical Deformation for Soft Robots.

Soft robots have the advantage of adaptability and flexibility in various scenarios and tasks due to their inherent flexibility and mouldability, which makes them highly promising for real-world applications. The development of electronic skin (E-skin) perception systems is crucial for the advancement of soft robots. However, achieving both exteroceptive and proprioceptive capabilities in E-skins, particularly in terms of decoupling and classifying sensing signals, remains a challenge. This study presents an E-skin with mixed electronic and ionic conductivity that can simultaneously achieve exteroceptive and proprioceptive, based on the resistance response of conductive hydrogels. It is integrated with soft robots to enable state perception, with the sensed signals further decoded using the machine learning model of decision trees and random forest algorithms. The results demonstrate that the newly developed hydrogel sensing system can accurately predict attitude changes in soft robots when subjected to varying degrees of pressing, hot pressing, bending, twisting, and stretching. These findings that multifunctional hydrogels combine with machine learning to decode signals may serve as a basis for improving the sensing capabilities of intelligent soft robots in future advancements.

Stagewise Training With Exponentially Growing Training Sets.

In the world of big data, training large-scale machine learning problems has gained considerable attention. Numerous innovative optimization strategies have been presented in recent years to accelerate the large-scale training process. However, the possibility of further accelerating the training process of various optimization algorithms remains an unresolved subject. To begin addressing this difficult problem, we exploit the researched findings that when training data are independent and identically distributed, the learning problem on a smaller dataset is not significantly different from the original one. Upon that, we propose a stagewise training technique that grows the size of the training set exponentially while solving nonsmooth subproblem. We demonstrate that our stagewise training via exponentially growing the size of the training sets (STEGSs) are compatible with a large number of proximal gradient descent and gradient hard thresholding (GHT) techniques. Interestingly, we demonstrate that STEGS can greatly reduce overall complexity while maintaining statistical accuracy or even surpassing the intrinsic error introduced by GHT approaches. In addition, we analyze the effect of the training data growth rate on the overall complexity. The practical results of applying l2,1 -and l0 -norms to a variety of large-scale real-world datasets not only corroborate our theories but also demonstrate the benefits of our STEGS framework.

Yes Associated Transcriptional Regulator 1 (YAP1) and WW Domain Containing Transcription Regulator (WWTR1) are required for murine pregnancy initiation.

Endometrial stromal cell decidualization is required for pregnancy success. Although this process is integral to fertility, many of the intricate molecular mechanisms contributing to decidualization remain undefined. One pathway that has been implicated in endometrial stromal cell decidualization in humans in vitro is the Hippo signaling pathway. Two previously conducted studies showed that the effectors of the Hippo signaling pathway, YAP1 and WWTR1, were required for decidualization of primary stromal cells in culture. To investigate the in vivo role of YAP1 and WWTR1 in decidualization and pregnancy initiation, we generated a Progesterone Cre mediated partial double knockout (pdKO) of Yap1 and Wwtr1. Female pdKOs exhibited subfertility, a compromised decidualization response, partial interruption in embryo transport, blunted endometrial receptivity, delayed implantation and subsequent embryonic development, and a unique transcriptional profile. Bulk mRNA sequencing revealed aberrant maternal remodeling evidenced by significant alterations in extracellular matrix proteins at 7.5 days post-coitus in pdKO dams and enrichment for terms associated with fertility-compromising diseases like pre-eclampsia and endometriosis. Our results indicate a required role for YAP1 and WWTR1 for successful mammalian uterine function and pregnancy success.

Brd4::Nutm1 fusion gene initiates NUT carcinoma in vivo.

NUT carcinoma (NC) is an aggressive cancer with no effective treatment. About 70% of NUT carcinoma is associated with chromosome translocation events that lead to the formation of a BRD4::NUTM1 fusion gene. Because the BRD4::NUTM1 gene is unequivocally cytotoxic when ectopically expressed in cell lines, questions remain on whether the fusion gene can initiate NC. Here, we report the first genetically engineered mouse model for NUT carcinoma that recapitulates the human t(15;19) chromosome translocation in mice. We demonstrated that the mouse t(2;17) syntenic chromosome translocation, forming the Brd4::Nutm1 fusion gene, could induce aggressive carcinomas in mice. The tumors present histopathological and molecular features similar to human NC, with enrichment of undifferentiated cells. Similar to the reports of human NC incidence, Brd4::Nutm1 can induce NC from a broad range of tissues with a strong phenotypical variability. The consistent induction of poorly differentiated carcinoma demonstrated a strong reprogramming activity of BRD4::NUTM1. The new mouse model provided a critical preclinical model for NC that will lead to better understanding and therapy development for NC.

Asynchronous Parallel Large-Scale Gaussian Process Regression.

Gaussian process regression (GPR) is an important nonparametric learning method in machine learning research with many real-world applications. It is well known that training large-scale GPR is a challenging task due to the required heavy computational cost and large volume memory. To address this challenging problem, in this article, we propose an asynchronous doubly stochastic gradient algorithm to handle the large-scale training of GPR. We formulate the GPR to a convex optimization problem, i.e., kernel ridge regression. After that, in order to efficiently solve this convex kernel problem, we first use the random feature mapping method to approximate the kernel model and then utilize two unbiased stochastic approximations, i.e., stochastic variance reduced gradient and stochastic coordinate descent, to update the solution asynchronously and in parallel. In this way, our algorithm scales well in both sample size and dimensionality, and speeds up the training computation. More importantly, we prove that our algorithm has a global linear convergence rate. Our experimental results on eight large-scale benchmark datasets with both regression and classification tasks show that the proposed algorithm outperforms the existing state-of-the-art GPR methods.

Early hippocampal high-amplitude rhythmic spikes predict post-traumatic epilepsy in mice.

Oscillations, a highly conserved brain function across mammalian species, are pivotal in brain physiology and pathology. Traumatic brain injury (TBI) often leads to subacute and chronic brain oscillatory alterations associated with complications like post-traumatic epilepsy (PTE) in patients and animal models. Our recent work longitudinally recorded local field potential from the contralateral hippocampus of 12 strains of recombinant inbred Collaborative Cross (CC) mice alongside classical laboratory inbred C57BL/6J mice after lateral fluid percussion injury. In this study, we profiled the acute (<12 hr post-injury) and subacute (12-48 hr post-injury) hippocampal oscillatory responses to TBI and evaluated their predictive value for PTE. We found dynamic high-amplitude rhythmic spikes with elevated power density and reduced entropy that prevailed during the acute phase in CC031 mice who later developed PTE. This characteristic early brain oscillatory alteration is absent in CC031 sham controls or other CC and reference C57BL/6J strains that did not develop PTE after TBI. Our work provides quantitative measures linking early brain oscillation to PTE at a population level in mice under controlled experimental conditions. These findings will offer insights into circuit mechanisms and potential targets for neuromodulatory intervention.

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study.

Endoscopy is the primary modality for detecting asymptomatic esophageal squamous cell carcinoma (ESCC) and precancerous lesions. Improving detection rate remains challenging. We developed a system based on deep convolutional neural networks (CNNs) for detecting esophageal cancer and precancerous lesions [high-risk esophageal lesions (HrELs)] and validated its efficacy in improving HrEL detection rate in clinical practice (trial registration ChiCTR2100044126 at www.chictr.org.cn). Between April 2021 and March 2022, 3117 patients ≥50 years old were consecutively recruited from Taizhou Hospital, Zhejiang Province, and randomly assigned 1:1 to an experimental group (CNN-assisted endoscopy) or a control group (unassisted endoscopy) based on block randomization. The primary endpoint was the HrEL detection rate. In the intention-to-treat population, the HrEL detection rate [28 of 1556 (1.8%)] was significantly higher in the experimental group than in the control group [14 of 1561 (0.9%), P = 0.029], and the experimental group detection rate was twice that of the control group. Similar findings were observed between the experimental and control groups [28 of 1524 (1.9%) versus 13 of 1534 (0.9%), respectively; P = 0.021]. The system's sensitivity, specificity, and accuracy for detecting HrELs were 89.7, 98.5, and 98.2%, respectively. No adverse events occurred. The proposed system thus improved HrEL detection rate during endoscopy and was safe. Deep learning assistance may enhance early diagnosis and treatment of esophageal cancer and may become a useful tool for esophageal cancer screening.