A lineage-resolved cartography of microRNA promoter activity in C. elegans empowers multidimensional developmental analysis.

Elucidating the expression of microRNAs in developing single cells is critical for functional discovery. Here, we construct scCAMERA (single-cell cartography of microRNA expression based on reporter assay), utilizing promoter-driven fluorescent reporters in conjunction with imaging and lineage tracing. The cartography delineates the transcriptional activity of 54 conserved microRNAs in lineage-resolved single cells throughout C. elegans embryogenesis. The combinatorial expression of microRNAs partitions cells into fine clusters reflecting their function and anatomy. Notably, the expression of individual microRNAs exhibits high cell specificity and divergence among family members. Guided by cellular expression patterns, we identify developmental functions of specific microRNAs, including miR-1 in pharynx development and physiology, miR-232 in excretory canal morphogenesis by repressing NHR-25/NR5A, and a functional synergy between miR-232 and miR-234 in canal development, demonstrating the broad utility of scCAMERA. Furthermore, integrative analysis reveals that tissue-specific fate determinants activate microRNAs to repress protein production from leaky transcripts associated with alternative, especially neuronal, fates, thereby enhancing the fidelity of developmental fate differentiation. Collectively, our study offers rich opportunities for multidimensional expression-informed analysis of microRNA biology in metazoans.

Image Analysis-Based Machine Learning for the Diagnosis of Retinopathy of Prematurity: A Meta-analysis and Systematic Review.

TOPIC: To evaluate the performance of machine learning (ML) in the diagnosis of retinopathy of prematurity (ROP) and to assess whether it can be an effective automated diagnostic tool for clinical applications. CLINICAL RELEVANCE: Early detection of ROP is crucial for preventing tractional retinal detachment and blindness in preterm infants, which has significant clinical relevance. METHODS: Web of Science, PubMed, Embase, IEEE Xplore, and Cochrane Library were searched for published studies on image-based ML for diagnosis of ROP or classification of clinical subtypes from inception to October 1, 2022. The quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies was used to determine the risk of bias (RoB) of the included original studies. A bivariate mixed effects model was used for quantitative analysis of the data, and the Deek's test was used for calculating publication bias. Quality of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation. RESULTS: Twenty-two studies were included in the systematic review; 4 studies had high or unclear RoB. In the area of indicator test items, only 2 studies had high or unclear RoB because they did not establish predefined thresholds. In the area of reference standards, 3 studies had high or unclear RoB. Regarding applicability, only 1 study was considered to have high or unclear applicability in terms of patient selection. The sensitivity and specificity of image-based ML for the diagnosis of ROP were 93% (95% confidence interval [CI]: 0.90-0.94) and 95% (95% CI: 0.94-0.97), respectively. The area under the receiver operating characteristic curve (AUC) was 0.98 (95% CI: 0.97-0.99). For the classification of clinical subtypes of ROP, the sensitivity and specificity were 93% (95% CI: 0.89-0.96) and 93% (95% CI: 0.89-0.95), respectively, and the AUC was 0.97 (95% CI: 0.96-0.98). The classification results were highly similar to those of clinical experts (Spearman's R = 0.879). CONCLUSIONS: Machine learning algorithms are no less accurate than human experts and hold considerable potential as automated diagnostic tools for ROP. However, given the quality and high heterogeneity of the available evidence, these algorithms should be considered as supplementary tools to assist clinicians in diagnosing ROP. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

A full-body transcription factor expression atlas with completely resolved cell identities in C. elegans.

Invariant cell lineage in C. elegans enables spatiotemporal resolution of transcriptional regulatory mechanisms controlling the fate of each cell. Here, we develop RAPCAT (Robust-point-matching- And Piecewise-affine-based Cell Annotation Tool) to automate cell identity assignment in three-dimensional image stacks of L1 larvae and profile reporter expression of 620 transcription factors in every cell. Transcription factor profile-based clustering analysis defines 80 cell types distinct from conventional phenotypic cell types and identifies three general phenotypic modalities related to these classifications. First, transcription factors are broadly downregulated in quiescent stage Hermaphrodite Specific Neurons, suggesting stage- and cell type-specific variation in transcriptome size. Second, transcription factor expression is more closely associated with morphology than other phenotypic modalities in different pre- and post-differentiation developmental stages. Finally, embryonic cell lineages can be associated with specific transcription factor expression patterns and functions that persist throughout postembryonic life. This study presents a comprehensive transcription factor atlas for investigation of intra-cell type heterogeneity.

HSSNet: A End-to-End Network for Detecting Tiny Targets of Apple Leaf Diseases in Complex Backgrounds.

Apple leaf diseases are one of the most important factors that reduce apple quality and yield. The object detection technology based on deep learning can detect diseases in a timely manner and help automate disease control, thereby reducing economic losses. In the natural environment, tiny apple leaf disease targets (a resolution is less than 32 × 32 pixel2) are easily overlooked. To address the problems of complex background interference, difficult detection of tiny targets and biased detection of prediction boxes that exist in standard detectors, in this paper, we constructed a tiny target dataset TTALDD-4 containing four types of diseases, which include Alternaria leaf spot, Frogeye leaf spot, Grey spot and Rust, and proposed the HSSNet detector based on the YOLOv7-tiny benchmark for professional detection of apple leaf disease tiny targets. Firstly, the H-SimAM attention mechanism is proposed to focus on the foreground lesions in the complex background of the image. Secondly, SP-BiFormer Block is proposed to enhance the ability of the model to perceive tiny targets of leaf diseases. Finally, we use the SIOU loss to improve the case of prediction box bias. The experimental results show that HSSNet achieves 85.04% mAP (mean average precision), 67.53% AR (average recall), and 83 FPS (frames per second). Compared with other standard detectors, HSSNet maintains high real-time detection speed with higher detection accuracy. This provides a reference for the automated control of apple leaf diseases.

Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review.

Background: Melanomas, the most common human malignancy, are primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy, and histopathological examination. We aimed to systematically review the performance and quality of machine learning-based methods in distinguishing melanoma and benign nevus in the relevant literature. Method: Four databases (Web of Science, PubMed, Embase, and the Cochrane library) were searched to retrieve the relevant studies published until March 26, 2022. The Predictive model Deviation Risk Assessment tool (PROBAST) was used to assess the deviation risk of opposing law. Result: This systematic review included thirty researches with 114007 subjects and 71 machine learning models. The convolutional neural network was the main machine learning method. The pooled sensitivity was 85% (95% CI 82-87%), the specificity was 86% (82-88%), and the C-index was 0.87 (0.84-0.90). Conclusion: The findings of our study showed that ML algorithms had high sensitivity and specificity for distinguishing between melanoma and benign nevi. This suggests that state-of-the-art ML-based algorithms for distinguishing melanoma from benign nevi may be ready for clinical use. However, a large proportion of the earlier published studies had methodological flaws, such as lack of external validation and lack of clinician comparisons. The results of these studies should be interpreted with caution.

Establishment and validation of a nomogram for predicting immune-related prognostic features in trunk melanoma-specific death.

Background: Trunk melanoma is one of the most common and deadly types of melanomas. Multiple factors are associated with the prognosis of patients with trunk melanoma. Currently, direct, and reliable clinical tools for early assessment of individual specific risk of death are limited, and most of them are prediction models for all-cause death. Their accuracy in predicting competitiveness events, which make up a relatively large portion, may be substantially compromised. Hence, we conducted this study to investigate the risk factors of trunk melanoma-specific death to establish a comprehensive prediction model suitable for clinical application. Methods: Patients with trunk melanoma analyzed in this study were from the SEER program [2010-2015]. The random sampling method was used to split the included cases into the training and validation cohorts at a ratio of 7:3. Univariate and multivariate competing risk models were used to screen the independent influencing factors of specific death, and then a nomogram covering these independent predictors was constructed. The concordance index (C-index) and a calibration curve were used to evaluate the calibration degree and accuracy of the nomogram. Results: We identified 21,198 patients with trunk melanoma from the SEER database, and 3,814 of them died (17.99%). Among the death cases, deaths from other causes accounted for 66.50%The prognostic nomogram included 8 variables and 16 independent influencing factors. The overall C-index in the training set was 0.89, and the receiver operating characteristic (ROC) curve for predicting 1-, 3-, and 5-year survival was 0.928 [95% confidence interval (CI): 0.911-0.945], 0.907 (95% CI: 0.895-0.918), and 0.891 (95% CI: 0.879-0.902), respectively. The C-index of the model in the validation set was 0.89, and the area under the ROC curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific death (CSD) was 0.927 (95% CI: 0.899-0.955), 0.916 (95% CI: 0.901-0.930), and 0.905 (95% CI: 0.899-0.921). Both the training set and the validation set showed the ideal calibration degree. Conclusions: This model can be used as a potential tool for prognostic risk management of trunk melanoma in the presence of many competing events.

Effect of tetracaine on dynamic reorganization of lipid membranes.

To understand the intrinsic influence of a drug on lipid membranes is of critical importance in pharmacological science. Herein, we report fluorescence microscopy analysis of the interaction between the local anesthetic tetracaine (TTC) and planar supported lipid bilayers (SLBs), as model membranes. Our results show that TTC increases lipid chain mobility, destabilizes the SLBs and remarkably induces membrane disruption and solubilization. Upon TTC binding, a local curvature change in the bilayer was observed, which led to the subsequent formation of up to 20-µm-long flexible lipid tubules as well as the formation of micron-size holes. Quantitative analysis revealed that membrane solubilization process can be divided into two distinct different stages as a function of TTC concentration. In the first stage (<800 µM), the bilayer disruption profiles fit well to a Langmuir isotherm, while in the second stage (800 µM-25 mM), TTC solubilizes the membrane in a detergent-like manner. Notably, the onset of membrane solubilization occurred below the critical micelle concentration (cmc) of TTC, indicating a local accumulation of the drug in the membrane. Additionally, cholesterol increases the insertion of TTC into the membrane and thus promotes the solubilization effect of TTC on lipid bilayers. These findings may help to elucidate the possible mechanisms of TTC interaction with lipid membranes, the dose dependent toxicity attributed to local anesthetics, as well as provide valuable information for drug development and modification.

Comparative Study on Neutron Irradiation Sensitization Effects of Nucleotide Borate Esters and Several Other Boron Agents.

More effective boron-containing compounds are needed for use in boron neutron capture therapy (BNCT). Here, borate esters were synthesized by heating and dehydrating nucleotides adenosine triphosphate (ATP), adenosine diphosphate (ADP), adenosine monophosphate (AMP), the nucleoside (inosine) or glycerol in the presence of boric acid (H3BO3). Borate ester products were compared to clinical boron agent boronophenylalanine (BPA) and several other borate esters for neutron-sensitization effects using the A549 cell line. Cells were incubated with boron agent solutions (2.3 mM) for 5 h, then washed, resuspended in fresh media, and irradiated with a neutron dose of 0.33 Sv followed by cell survival assessment using the CCK-8 method. Calculated radiosensitization values (control group cell survival rate/boron agent-treated experimental group cell survival rate) were 3.9 ± 0.2 (ATP borate ester), 2.4 ± 0.1 (BPA), 2.1 ± 0.1 (ADP borate ester), 1.9 ± 0.2 (AMP borate ester), 1.7 ± 0.3 (glycerin borate ester), 1.4 ± 0.1 (inosine borate ester), 1.3 ± 0.3 (triethanolamine borate ester) and 1.3 ± 0.5 (H3BO3). Borate esters derived from nucleotides ATP, ADP or AMP exhibited significantly higher sensitization values than did those derived from glycerol, inosine or triethanolamine. Notably, due to its relatively higher water solubility and degree of tumor cell enrichment, ATP borate ester exhibited the highest sensitization rate overall, significantly exceeding rates obtained for BPA and borate esters of ADP and AMP. Flow cytometric determinations of boron agent-treated cell survival at 24 h postirradiation revealed long-term apoptosis rates of 4.8-6.6 ± 0.2% (nucleotide borate ester groups) and 5.6 ± 0.3% (BPA group) compared to 3.9 ± 0.1% (irradiation control group without boron agent) and 2.6 ± 0.2% (blank control group). Significant differences between experimental and control groups demonstrated that nucleotide borate esters and BPA induced long-term radiosensitization effects. In particular, postirradiation percentages of ATP borate ester-treated cells progressing to DNA replication prophase (G1 phase) increased significantly, while percentages of cells progressing to S phase significantly decreased, demonstrating cellular DNA replication inhibition. Meanwhile, boron content values of tumor tissue, measured using inductively coupled plasma mass spectrometry (ICP-MS) and expressed as tumor-to-normal tissue boron ratios (T/N), were not significantly different between nucleotide borate ester- and BPA-fed groups of tumor-bearing mice. However, tumor tissue boron concentrations of nucleotide borate ester-fed mice (0.81-0.88 ± 0.04 µg/g) significantly exceeded those of BPA-fed mice (0.52 ± 0.05 µg/g) and thus provided greater tumor tissue boron enrichment for achieving a stronger neutron radiation-sensitizing effect. In conclusion, nucleotide borate esters, especially ATP borate ester, exhibited superior neutron radiosensitization effects than did other representative borate ester compounds and significantly greater long-term radiation effects as well. Thus, nucleotide borate esters have several advantages over other borate esters for BNCT and therefore warrant further study.