Motion analysis comparing surface imaging and diaphragm tracking on kV projections for deep inspiration breath hold (DIBH).

BACKGROUND: Surface-guided imaging (SGI) is increasingly utilized to monitor patient motion during deep inspiration breath hold (DIBH) in radiotherapy. Understanding the association between surface and internal motion is crucial for effective monitoring. PURPOSE: To investigate the relation between motion detected by SGI using surface-guided radiotherapy (SGRT) and internal motion measured through diaphragm tracking on kV projections acquired with DIBH for online CBCT. METHODS: Both SGI and kV were simultaneously acquired for ten patients over a total of 200 breath holds (BH). Diaphragm tracking was performed using second-degree polynomial curve fitting on the derivative images for each kV projection and high-pass filtering at 1/30 Hz to remove rotational effects. The superior-inferior (SI) and anterior-posterior (AP) motions of SGI were then compared to kV tracking using various statistical measures. RESULTS: The correlation (individuals' median: -0.07 to 0.73) was a suboptimal metric for the BH data. The median and 95th percentile absolute differences between SGI-SI and kV were 0.73 mm and 3.46 mm, respectively, during DIBH. For SGI-AP, the corresponding values were 0.55 mm and 2.80 mm. For inter-BH measurements, the contingency table based on a 3 mm threshold indicated surface/diaphragm motion agreement for SGI-SI/kV and SGI-AP/kV was 61 % and 56 %, respectively. CONCLUSION: Both intra- and inter-BH measurements indicated a limited association between surface and diaphragm motion, with certain constraints noted due to kV tracking and DIBH data. These findings warrant further investigation into the association between surface and internal motion.

Astragalus polysaccharides sensitize ovarian cancer stem cells to PARPi by inhibiting mitophagy via PINK1/Parkin signaling.

Ovarian cancer (OC) remains the most lethal gynecological malignant tumor. PARP inhibitors (PARPi) have significantly improved survival, particularly in patients with OC with BRCA1/2 mutations. However, the majority of patients eventually develop resistance to PARPi. Cancer stem cells (CSCs) are considered the source of drug resistance in cancer. Our study found that the synergistic effect of astragalus polysaccharides (APSs) and PARPi was observed in ovarian cancer stem cells (OCSCs) by decreasing cell viability and self-renewal potential while inducing apoptosis. The present study also demonstrated that OCSCs had increased mitophagy. Furthermore, it was observed that APS in combination with PARPi inhibits mitophagy and downregulates the PINK1 protein level in OCSCs. The overexpression of PINK1 via the pEGFP(+)-PINK1 plasmid resulted in a partial reversal of the increased susceptibility of OCSCs when PARPi were administrated concurrently with APS. In conclusion, APS increases OCSC sensitivity to PARPi by inhibiting mitophagy via the PINK1/Parkin pathway regulation.

Molecular mechanism and functional significance of Wapl interaction with the Cohesin complex.

The ring-shaped Cohesin complex, consisting of core subunits Smc1, Smc3, Scc1, and SA2 (or its paralog SA1), topologically entraps two duplicated sister DNA molecules to establish sister chromatid cohesion in S-phase. It remains largely elusive how the Cohesin release factor Wapl binds the Cohesin complex, thereby inducing Cohesin disassociation from mitotic chromosomes to allow proper resolution and separation of sister chromatids. Here, we show that Wapl uses two structural modules containing the FGF motif and the YNARHWN motif, respectively, to simultaneously bind distinct pockets in the extensive composite interface between Scc1 and SA2. Strikingly, only when both docking modules are mutated, Wapl completely loses the ability to bind the Scc1-SA2 interface and release Cohesin, leading to erroneous chromosome segregation in mitosis. Surprisingly, Sororin, which contains a conserved FGF motif and functions as a master antagonist of Wapl in S-phase and G2-phase, does not bind the Scc1-SA2 interface. Moreover, Sgo1, the major protector of Cohesin at mitotic centromeres, can only compete with the FGF motif but not the YNARHWN motif of Wapl for binding Scc1-SA2 interface. Our data uncover the molecular mechanism by which Wapl binds Cohesin to ensure precise chromosome segregation.

CircMAN1A2_009 facilitates YBX1 nuclear localization to induce GLO1 activation for cervical adenocarcinoma cell growth.

The molecular mechanisms driving the development of cervical adenocarcinoma (CADC) and optimal patient management strategies remain elusive. In this study, we have identified circMAN1A2_009 as an oncogenic circular RNA (circRNA) in CADC. Clinically, circMAN1A2_009 showed significant upregulation in CADC tissues, with an impressive area under the curve value of 0.8075 for detecting CADC. Functional studies, involving both gain-of-function and loss-of-function experiments, revealed that circMAN1A2_009 suppressed reactive oxygen species accumulation and apoptosis, and boosted cell viability in CADC cells. Conversely, silencing circMAN1A2_009 reversed these effects. Further mechanistic investigations indicated that circMAN1A2_009 interacted with YBX1, facilitating the phosphorylation levels of YBX1 at serine 102 (p-YBX1S102) and facilitating YBX1 nuclear localization through sequence 245-251. This interaction subsequently increased the activity of the glyoxalase 1 (GLO1) promoter, leading to the activation of GLO1 expression. Consistently, inhibition of either YBX1 or GLO1 mirrored the biological effects of circMAN1A2_009 in CADC cells. Additionally, knockdown of YBX1 or GLO1 partially reversed the oncogenic behaviors induced by circMAN1A2_009. In conclusion, our findings propose circMAN1A2_009 as a potential oncogene and a promising indicator for diagnosing and guiding therapy in CADC patients.

RAD51B-AS1 promotes the malignant biological behavior of ovarian cancer through upregulation of RAD51B. / RAD51B-AS1通过上调RAD51B促进卵巢癌的恶性生物学行为.

Long non-coding RNAs (lncRNAs) play an indispensable role in the occurrence and development of ovarian cancer (OC). However, the potential involvement of lncRNAs in the progression of OC is largely unknown. To investigate the detailed roles and mechanisms ofRAD51 homolog B-antisense 1 (RAD51B-AS1), a novel lncRNA in OC, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to verify the expression of RAD51B-AS1. Cellular proliferation, metastasis, and apoptosis were detected using the cell counting kit-8 (CCK-8), colony-formation, transwell, and flow cytometry assays. Mouse xenograft models were established for the detection of tumorigenesis. The results revealed that RAD51B-AS1 was significantly upregulated in a highly metastatic human OC cell line and OC tissues. RAD51B-AS1 significantly increased the proliferation and metastasis of OC cells and enhanced their resistance to anoikis. Biogenetics prediction analysis revealed that the only target gene of RAD51B-AS1 was RAD51B. Subsequent gene function experiments revealed that RAD51B exerts the same biological effects as RAD51B-AS1. Rescue experiments demonstrated that the malignant biological behaviors promoted by RAD51B-AS1 overexpression were partially or completely reversed by RAD51B silencing in vitro and in vivo. Thus, RAD51B-AS1 promotes the malignant biological behaviors of OC and activates the protein kinase B (Akt)/B cell lymphoma protein-2 (Bcl-2) signaling pathway, and these effects may be associated with the positive regulation of RAD51B expression. RAD51B-AS1 is expected to serve as a novel molecular biomarker for the diagnosis and prediction of poor prognosis in OC, and as a potential therapeutic target for disease management.

Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine.

Background: Serous ovarian carcinoma (SOC) is considered the most lethal gynecological malignancy. The current lack of reliable prognostic biomarkers for SOC reduces the efficacy of predictive, preventive, and personalized medicine (PPPM/3PM) in patients with SOC, leading to unsatisfactory therapeutic outcomes. N6-methyladenosine (m6A) modification-associated long noncoding RNAs (lncRNAs) are effective predictors of SOC. In this study, an effective risk prediction model for SOC was constructed based on m6A modification-associated lncRNAs. Methods: Transcriptomic data and clinical information of patients with SOC were downloaded from The Cancer Genome Atlas. Candidate lncRNAs were identified using univariate and multivariate and least absolute shrinkage and selection operator-penalized Cox regression analyses. The molecular mechanisms of m6A effector-related lncRNAs were explored via Gene Ontology, pathway analysis, gene set enrichment analysis, and gene set variation analysis (GSVA). The extent of immune cell infiltration was assessed using various algorithms, including CIBERSORT, Microenvironment Cell Populations counter, xCell, European Prospective Investigation into Cancer and Nutrition, and GSVA. The calcPhenotype algorithm was used to predict responses to the drugs commonly used in ovarian carcinoma therapy. In vitro experiments, such as migration and invasion Transwell assays, wound healing assays, and dot blot assays, were conducted to elucidate the functional roles of candidate lncRNAs. Results: Six m6A effector-related lncRNAs that were markedly associated with prognosis were used to establish an m6A effector-related lncRNA risk model (m6A-LRM) for SOC. Immune microenvironment analysis suggested that the high-risk group exhibited a proinflammatory state and displayed increased sensitivity to immunotherapy. A nomogram was constructed with the m6A effector-related lncRNAs to assess the prognostic value of the model. Sixteen drugs potentially targeting m6A effector-related lncRNAs were identified. Furthermore, we developed an online web application for clinicians and researchers (https://leley.shinyapps.io/OC_m6A_lnc/). Overexpression of the lncRNA RP11-508M8.1 promoted SOC cell migration and invasion. METTL3 is an upstream regulator of RP11-508M8.1. The preliminary regulatory axis METTL3/m6A/RP11-508M8.1/hsa-miR-1270/ARSD underlying SOC was identified via a combination of in vitro and bioinformatic analyses. Conclusion: In this study, we propose an innovative prognostic risk model and provide novel insights into the mechanism underlying the role of m6A-related lncRNAs in SOC. Incorporating the m6A-LRM into PPPM may help identify high-risk patients and personalize treatment as early as possible.

Performance verification of a biochemical detection system for hydrothorax and ascites and clinical diagnostic accuracy evaluation of exudate and tuberculous effusion.

Background: Lactate dehydrogenase (LDH), total protein (TP) and glucose (Glu) in pleural hydrothorax and ascites can be used in the diagnosis of exudate, and adenosine deaminase (ADA) can be used in the diagnosis of tuberculous effusion. However, the manufacturers do not claim that their biochemical reagents can be used to detect hydrothorax and ascites samples. Therefore, medical laboratories must conduct suitability studies on biochemical reagents for hydrothorax and ascites samples to comply with regulatory requirements for humor detection. This study aimed to verify the analytical performance and clinical diagnostic accuracy of the Mindray biochemical reagents, including LDH, TP, Glu and ADA, for hydrothorax and ascites. Methods: The repeatability, detection limits and reference intervals of Mindray biochemical reagents (LDH, TP, Glu, ADA) in detecting hydrothorax and ascites were determined. The comparison of different measurement procedures was performed. Meanwhile, the diagnostic accuracy of LDH, TP, Glu and ADA were assessed. Results: The quality control results of LDH, TP, Glu, and ADA were all under control. The repeatability coefficient of variation (%) of LDH, TP, Glu, and ADA were all less than 1%. The limits of blank of LDH, TP, Glu, and ADA were 0.33 U/L, 0.45 g/L, 0.00 mmol/L, and 0.04 U/L, respectively; the limits of detection were 1.57 U/L, 1.85 g/L, 0.05 mmol/L, and 0.12 U/L, respectively. Compared with the reference measurement program, the correlation coefficients of LDH, TP, Glu and ADA were 0.9931, 0.9983, 0.9996 and 0.9966, respectively; the regression equations were y=1.0082x-10.06, y=0.9965x-0.4732, y=0.9903x+0.0522 and y=1.0051x-0.0232, respectively. The reference intervals of LDH, TP, Glu, and ADA in hydrothorax and ascites were ≤198.39 U/L, ≤32.97 g/L, ≥5.03 mmol/L. and ≤11.00 U/L respectively. For differentiating between exudates and transudates, the area under the curve (AUC) of LDH, TP, and Glu were 0.913, 0.875, and 0.767, respectively; the AUC of ADA for the differential diagnosis of tuberculous and nontuberculous effusions was 0.876. Conclusions: The LDH, TP, Glu, and ADA assays were validated for use with the Mindray BS-2800 analyzer for hydrothorax and ascites evaluation. LDH, TP, and Glu in hydrothorax and ascites are applicable to the differential diagnosis of exudates and transudates; ADA in hydrothorax and ascites can be employed to differentiate and diagnose tuberculous and nontuberculous effusions.

Investigation of intra-fractionated range guided adaptive proton therapy (RGAPT): II. Range-shift compensated on-line treatment adaptation and verification.

We previously proposed range-guided adaptive proton therapy (RGAPT) that uses mid-range treatment beams as probing beams and intra-fractionated range measurements for online adaptation. In this work, we demonstrated experimental verification and reported the dosimetric accuracy for RGAPT. A STEEV phantom was used for the experiments, and a 3 × 3 × 3 cm3cube inside the phantom was assigned to be the treatment target. We simulated three online range shift scenarios: reference, overshoot, and undershoot, by placing upstream Lucite sheets, 4, 0, and 8 that corresponded to changes of 0, 6.8, and -6.8 mm, respectively, in water-equivalent path length. The reference treatment plan was to deliver single-field uniform target doses in pencil beam scanning mode and generated on the Eclipse treatment planning system. Different numbers of mid-range layers, including single, three, and five layers, were selected as probing beams to evaluate beam range (BR) measurement accuracy in positron emission tomography (PET). Online plans were modified to adapt to BR shifts and compensate for probing beam doses. In contrast, non-adaptive plans were also delivered and compared to adaptive plans by film measurements. The mid-range probing beams of three (5.55MU) and five layers (8.71MU) yielded accurate range shift measurements in 60 s of PET acquisition with uncertainty of 0.5 mm while the single-layer probing (1.65MU) was not sufficient for measurements. The adaptive plans achieved an average gamma (2%/2 mm) passing rate of 95%. In contrast, the non-adaptive plans only had an average passing rate of 69%. RGAPT planning and delivery are feasible and verified by the experiments. The probing beam delivery, range measurements, and adaptive planning and delivery added a small increase in treatment delivery workflow time but resulted in substantial dose improvement. The three-layer mid-range probing was most suitable considering the balance of high range measurement accuracy and the low number of probing beam layers.

Investigation of intra-fractionated range guided adaptive proton therapy: I. On-line PET imaging and range measurement.

Objective.Develop a prototype on-line positron emission tomography (PET) scanner and evaluate its capability of on-line imaging and intra-fractionated proton-induced radioactivity range measurement.Approach.Each detector consists of 32 × 32 array of 2 × 2 × 30 mm3Lutetium-Yttrium Oxyorthosilicate scintillators with single-scintillator-end readout through a 20 × 20 array of 3 × 3 mm2Silicon Photomultipliers. The PET can be configurated with a full-ring of 20 detectors for conventional PET imaging or a partial-ring of 18 detectors for on-line imaging and range measurement. All detector-level readout and processing electronics are attached to the backside of the system gantry and their output signals are transferred to a field-programable-gate-array based system electronics and data acquisition that can be placed 2 m away from the gantry. The PET imaging performance and radioactivity range measurement capability were evaluated by both the offline study that placed a radioactive source with known intensity and distribution within a phantom and the online study that irradiated a phantom with proton beams under different radiation and imaging conditions.Main results.The PET has 32 cm diameter and 6.5 cm axial length field-of-view (FOV), ∼2.3-5.0 mm spatial resolution within FOV, 3% sensitivity at the FOV center, 18%-30% energy resolution, and ∼9 ns coincidence time resolution. The offline study shows the PET can determine the shift of distal falloff edge position of a known radioactivity distribution with the accuracy of 0.3 ± 0.3 mm even without attenuation and scatter corrections, and online study shows the PET can measure the shift of proton-induced positron radioactive range with the accuracy of 0.6 ± 0.3 mm from the data acquired with a short-acquisition (60 s) and low-dose (5 MU) proton radiation to a human head phantom.Significance.This study demonstrated the capability of intra-fractionated PET imaging and radioactivity range measurement and will enable the investigation on the feasibility of intra-fractionated, range-shift compensated adaptive proton therapy.

Proton 3D dose measurement with a multi-layer strip ionization chamber (MLSIC) device.

Objective. In current clinical practice for quality assurance (QA), intensity modulated proton therapy (IMPT) fields are verified by measuring planar dose distributions at one or a few selected depths in a phantom. A QA device that measures full 3D dose distributions at high spatiotemporal resolution would be highly beneficial for existing as well as emerging proton therapy techniques such as FLASH radiotherapy. Our objective is to demonstrate feasibility of 3D dose measurement for IMPT fields using a dedicated multi-layer strip ionization chamber (MLSIC) device.Approach.Our developed MLSIC comprises a total of 66 layers of strip ion chamber (IC) plates arranged, alternatively, in thexandydirection. The first two layers each has 128 channels in 2 mm spacing, and the following 64 layers each has 32/33 IC strips in 8 mm spacing which are interconnected every eight channels. A total of 768-channel IC signals are integrated and sampled at a speed of 6 kfps. The MLSIC has a total of 19.2 cm water equivalent thickness and is capable of measurement over a 25 × 25 cm2field size. A reconstruction algorithm is developed to reconstruct 3D dose distribution for each spot at all depths by considering a double-Gaussian-Cauchy-Lorentz model. The 3D dose distribution of each beam is obtained by summing all spots. The performance of our MLSIC is evaluated for a clinical pencil beam scanning (PBS) plan.Main results.The dose distributions for each proton spot can be successfully reconstructed from the ionization current measurement of the strip ICs at different depths, which can be further summed up to a 3D dose distribution for the beam. 3D Gamma Index analysis indicates acceptable agreement between the measured and expected dose distributions from simulation, Zebra and MatriXX.Significance.The dedicated MLSIC is the first pseudo-3D QA device that can measure 3D dose distribution in PBS proton fields spot-by-spot.

A non-canonical role of the inner kinetochore in regulating sister-chromatid cohesion at centromeres.

The 16-subunit Constitutive Centromere-associated Network (CCAN)-based inner kinetochore is well-known for connecting centromeric chromatin to the spindle-binding outer kinetochore. Here, we report a non-canonical role for the inner kinetochore in directly regulating sister-chromatid cohesion at centromeres. We provide biochemical, X-ray crystal structure, and intracellular ectopic localization evidence that the inner kinetochore directly binds cohesin, a ring-shaped multi-subunit complex that holds sister chromatids together from S-phase until anaphase onset. This interaction is mediated by binding of the 5-subunit CENP-OPQUR sub-complex of CCAN to the Scc1-SA2 sub-complex of cohesin. Mutation in the CENP-U subunit of the CENP-OPQUR complex that abolishes its binding to the composite interface between Scc1 and SA2 weakens centromeric cohesion, leading to premature separation of sister chromatids during delayed metaphase. We further show that CENP-U competes with the cohesin release factor Wapl for binding the interface of Scc1-SA2, and that the cohesion-protecting role for CENP-U can be bypassed by depleting Wapl. Taken together, this study reveals an inner kinetochore-bound pool of cohesin, which strengthens centromeric sister-chromatid cohesion to resist metaphase spindle pulling forces.

Single-cell transcriptomics reveals the aggressive landscape of high-grade serous carcinoma and therapeutic targets in tumor microenvironment.

High-grade serous carcinoma (HGSC) is characterized by early abdominal metastasis, leading to a dismal prognosis. In this study, we conducted single-cell RNA sequencing on 109,573 cells from 34 tumor samples of 18 HGSC patients, including both primary tumors and their metastatic sites. Our analysis revealed a distinct S100A9+ tumor cell subtype present in both primary and metastatic sites, strongly associated with poor overall survival. This subtype exhibited high expression of S100A8, S100A9, ADGRF1, CEACAM6, CST6, NDRG2, MUC4, PI3, SDC1, and C15orf48. Individual knockdown of these ten marker genes, validated through in vitro and in vivo models, significantly inhibited ovarian cancer growth and invasion. Around S100A9+ tumor cells, a population of HK2+_CAF was identified, characterized by activated glycolysis metabolism, correlating with shorter overall survival in patients. Notably, similar to CAFs, immunosuppressive tumor-associated macrophage (TAM) subtypes underwent glycolipid metabolism reprogramming via PPARgamma regulation, promoting tumor metastasis. These findings shed light on the mechanisms driving the aggressiveness of HGSC, offering crucial insights for the development of novel therapeutic targets against this formidable cancer.

Prediction of ovarian cancer prognosis using statistical radiomic features of ultrasound images.

Objective. Ovarian cancer is the deadliest gynecologic malignancy worldwide. Ultrasound is the most useful non-invasive test for preoperative diagnosis of ovarian cancer. In this study, by leveraging multiple ultrasound images from the same patient to generate personalized, informative statistical radiomic features, we aimed to develop improved ultrasound image-based prognostic models for ovarian cancer.Approach. A total of 2057 ultrasound images from 514 ovarian cancer patients, including 355 patients with epithelial ovarian cancer, from two hospitals in China were collected for this study. The models were constructed using our recently developed Frequency Appearance in Multiple Univariate pre-Screening feature selection algorithm and Cox proportional hazards model.Main results. The models showed high predictive performance for overall survival (OS) and recurrence-free survival (RFS) in both epithelial and nonepithelial ovarian cancer, with concordance indices ranging from 0.773 to 0.794. Radiomic scores predicted 2 year OS and RFS risk groups with significant survival differences (log-rank test,P< 1.0 × 10-4for both validation cohorts). OS and RFS hazard ratios between low- and high-risk groups were 15.994 and 30.692 (internal cohort) and 19.339 and 19.760 (external cohort), respectively. The improved performance of these newly developed prognostic models was mainly attributed to the use of multiple preoperative ultrasound images from the same patient to generate statistical radiomic features, rather than simply using the largest tumor region of interest among them. The models also revealed that the roundness of tumor lesion shape was positively correlated with prognosis for ovarian cancer.Significance.The newly developed prognostic models based on statistical radiomic features from ultrasound images were highly predictive of the risk of cancer-related death and possible recurrence not only for patients with epithelial ovarian cancer but also for those with nonepithelial ovarian cancer. They thereby provide reliable, non-invasive markers for individualized prognosis evaluation and clinical decision-making for patients with ovarian cancer.

Niraparib maintenance therapy using an individualised starting dose in patients with platinum-sensitive recurrent ovarian cancer (NORA): final overall survival analysis of a phase 3 randomised, placebo-controlled trial.

Background: Niraparib significantly prolonged progression-free survival versus placebo in patients with platinum-sensitive, recurrent ovarian cancer (PSROC), regardless of germline BRCA mutation (gBRCAm) status, in NORA. This analysis reports final data on overall survival (OS). Methods: This randomised, double-blind, placebo-controlled, phase 3 trial enrolled patients across 30 centres in China between 26 September 2017 and 2 February 2019 (clinicaltrials.gov, NCT03705156). Eligible patients had histologically confirmed, recurrent, (predominantly) high-grade serous epithelial ovarian cancer, fallopian tube carcinoma, or primary peritoneal carcinoma (no histological restrictions for those with gBRCAm) and had received ≥2 prior lines of platinum-based chemotherapy. Patients were randomised (2:1) to receive niraparib or placebo, with stratification by gBRCAm status, time to recurrence following penultimate platinum-based chemotherapy, and response to last platinum-based chemotherapy. Following a protocol amendment, the starting dose was individualised: 200 mg/day for patients with bodyweight <77 kg and/or platelet count <150 × 103/µL at baseline and 300 mg/day otherwise. OS was a secondary endpoint. Findings: Totally, 265 patients were randomised to receive niraparib (n = 177) or placebo (n = 88), and 249 (94.0%) received an individualised starting dose. As of 14 August 2023, median follow-up for OS was 57.9 months (IQR, 54.8-61.6). Median OS (95% CI) with niraparib versus placebo was 51.5 (41.4-58.9) versus 47.6 (33.3-not evaluable [NE]) months, with hazard ratio [HR] of 0.86 (95% CI, 0.60-1.23), in the overall population; 56.0 (36.1-NE) versus 47.6 (31.6-NE) months, with HR of 0.86 (95% CI, 0.46-1.58), in patients with gBRCAm; and 46.5 (41.0-NE) versus 46.9 (31.8-NE) months, with HR of 0.87 (95% CI, 0.56-1.35), in those without. No new safety signals were identified, and myelodysplastic syndromes/acute myeloid leukaemia occurred in three (1.7%) niraparib-treated patients. Interpretation: Niraparib maintenance therapy with an individualised starting dose demonstrated a favourable OS trend versus placebo in PSROC patients, regardless of gBRCAm status. Funding: Zai Lab (Shanghai) Co., Ltd; National Major Scientific and Technological Special Project for "Significant New Drugs Development" in 2018, China [grant number 2018ZX09736019].

Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today?

Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences.

Differential expression of lymphocyte subpopulations in the peripheral blood of patients with COVID-19: Implications for disease severity and prognosis.

OBJECTIVE: To investigate the expression patterns and clinical significance of specific lymphocyte subsets in the peripheral blood of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. METHODS: Between December 2022 and February 2023, a cohort of 165 patients from the First Affiliated Hospital of Guangzhou University of Chinese Medicine were analyzed. The participants represented various stages of coronavirus infection severity: mild, moderate, severe, and critical. Additionally, 40 healthy individuals constituted the control group. The FC 500MPL flow cytometer and associated reagents for flow cytometry. RESULTS: Compared with the healthy control group, activated B lymphocytes witnessed a pronounced increase (p < .05). A significant decrease was observed in the levels of Breg, Cytotoxic T cells or Suppressor T-cell (Tc/s), late-activated T, late-activated Th, and late-activated Tc/s lymphocytes (p < .05). Th, initial Th, initial Tc/s, total Treg, natural Treg, induced Treg, early activated T, and early activated Th lymphocyte levels showed no significant difference (p > .05). As the disease progressed, there was an uptick in midterm activated T lymphocytes (p < .05), while Breg, T, Tc/s, senescent Tc/s, and total senescent T levels dwindled (p < .05). Noteworthy patterns emerged across different groups for B1, T-lymphocytes, Tc/s, B2, CD8+ Treg cells, and other subsets, highlighting variance in immune responses relative to disease severity. When juxtaposed, no significant difference was found in the expression levels of lymphocyte subsets between patients who died and those deemed critically ill (p > .05). CONCLUSION: Subsets of Treg and B-cells could act as yardsticks for the trajectory of SARS-CoV-2 infection and might have potential in forecasting patient trajectories. A comprehensive evaluation of lymphocyte subsets, especially in real-time, holds the key to discerning the clinical severity in those with COVID-19. This becomes instrumental in monitoring treatment outcomes, tracking disease evolution, and formulating prognostications. Moreover, the results provide a deeper understanding of the cellular immune defense mechanisms against the novel coronavirus infection.

Fatty acid metabolism-related lncRNA prognostic signature for serous ovarian carcinoma.

Background: To explore the role of fatty acid metabolism (FAM)-related lncRNAs in the prognosis and antitumor immunity of serous ovarian cancer (SOC). Materials & methods: A SOC FAM-related lncRNA risk model was developed and evaluated by a series of analyses. Additional immune-related analyses were performed to further assess the associations between immune state, tumor microenvironment and the prognostic risk model. Results: Five lncRNAs associated with the FAM genes were found and used to create a predictive risk model. The patients with a low-risk profile exhibited favorable prognostic outcomes. Conclusion: The established prognostic risk model exhibits better predictive capabilities for the prognosis of patients with SOC and offers novel potential therapy targets for SOC.

Fluorescence detecting glycopeptide antibiotics via a dynamic molecular switch.

BACKGROUND: Glycopeptide antibiotics (GPAs) represented by vancomycin (VAN) are clinically used as a first-line treatment for serious infections caused by Gram-positive pathogens. The use and dosing methods of GPAs are rigorously managed for safety considerations, which calls for fast and accurate quantification approaches. RESULT: A new sort of fluorescent probes for GPAs has been proposed, each of which was integrated by a fluorescein-based reporter and a GPAs' recognition peptide D-alanyl-D-alanine (D-Ala-D-Ala). These probes work as dynamic molecular switches, which mainly exist as non-fluorescent spirolactam forms in the absence of GPAs. GPAs binding with the dipeptide regulates the dynamic balance between fluorescence OFF lactam form and fluorescence ON ring-opened form, rendering these probes capable of GPAs detecting. The most promising one P1 exhibits excellent sensitivity and selectivity towards GPAs detection. SIGNIFICANCE: Different to previous developments, P1 consists of a single fluorophore without the need of a fluorescence-quenching group or a secondary dye, which is the smallest fluorescent probe for GPAs up to now. P1 realizes direct VAN quantification from complex biological samples including real serums, dispensing with additional drug extraction. More interestingly, both P1 and P6 can distinguish GPAs with different peptide backbones, which has not been achieved previously.

Exploring Deep Learning for Estimating the Isoeffective Dose of FLASH Irradiation From Mouse Intestinal Histological Images.

PURPOSE: Ultrahigh-dose-rate (FLASH) irradiation has been reported to reduce normal tissue damage compared with conventional dose rate (CONV) irradiation without compromising tumor control. This proof-of-concept study aims to develop a deep learning (DL) approach to quantify the FLASH isoeffective dose (dose of CONV that would be required to produce the same effect as the given physical FLASH dose) with postirradiation mouse intestinal histology images. METHODS AND MATERIALS: Eighty-four healthy C57BL/6J female mice underwent 16 MeV electron CONV (0.12 Gy/s; n = 41) or FLASH (200 Gy/s; n = 43) single fraction whole abdominal irradiation. Physical dose ranged from 12 to 16 Gy for FLASH and 11 to 15 Gy for CONV in 1 Gy increments. Four days after irradiation, 9 jejunum cross-sections from each mouse were hematoxylin and eosin stained and digitized for histological analysis. CONV data set was randomly split into training (n = 33) and testing (n = 8) data sets. ResNet101-based DL models were retrained using the CONV training data set to estimate the dose based on histological features. The classical manual crypt counting (CC) approach was implemented for model comparison. Cross-section-wise mean squared error was computed to evaluate the dose estimation accuracy of both approaches. The validated DL model was applied to the FLASH data set to map the physical FLASH dose into the isoeffective dose. RESULTS: The DL model achieved a cross-section-wise mean squared error of 0.20 Gy2 on the CONV testing data set compared with 0.40 Gy2 of the CC approach. Isoeffective doses estimated by the DL model for FLASH doses of 12, 13, 14, 15, and 16 Gy were 12.19 ± 0.46, 12.54 ± 0.37, 12.69 ± 0.26, 12.84 ± 0.26, and 13.03 ± 0.28 Gy, respectively. CONCLUSIONS: Our proposed DL model achieved accurate CONV dose estimation. The DL model results indicate that in the physical dose range of 13 to 16 Gy, the biologic dose response of small intestinal tissue to FLASH irradiation is represented by a lower isoeffective dose compared with the physical dose. Our DL approach can be a tool for studying isoeffective doses of other radiation dose modifying interventions.