A novel Bayesian generative approach for estimating tumor dynamics from published studies.

Tumor growth inhibition (TGI) modeling attempts to describe the time course changes in tumor size for patients undergoing cancer therapy. TGI models present several advantages over traditional exposure-response models that are based explicitly on clinical end points and have become a popular tool in the pharmacometrics community. Unfortunately, the data required to fit TGI models, namely longitudinal tumor measurements, are sparse or often not available in literature or publicly accessible databases. On the contrary, common end points such as progression-free survival (PFS) and objective response rate (ORR) are directly derived from longitudinal tumor measurements and are routinely published. To this end, a Bayesian generative model relating underlying tumor dynamics to summary PFS and ORR data is introduced to learn TGI model parameters using only published summary data. The parameterized model can describe the tumor dynamics, quantify treatment effect, and account for differences in the study population. The utility of this model is shown by applying it to several published studies, and learned parameters are combined to simulate an in silico trial of a novel combination therapy in a novel setting.

Machine learning for prediction of schizophrenia based on identifying the primary and interaction effects of minor physical anomalies.

In the neurodevelopmental model of schizophrenia, minor physical anomalies (MPAs) are considered neurodevelopmental markers of schizophrenia. To date, there has been no research to evaluate the interaction between MPAs. Our study built and used a machine learning model to predict the risk of schizophrenia based on measurements of MPA items and to investigate the potential primary and interaction effects of MPAs. The study included 470 patients with schizophrenia and 354 healthy controls. The models used are classical statistical model, Logistic Regression (LR), and machine leaning models, Decision Tree (DT) and Random Forest (RF). We also plotted two-dimensional scatter diagrams and three-dimensional linear/quadratic discriminant analysis (LDA/QDA) graphs for comparison with the DT dendritic structure. We found that RF had the highest predictive power for schizophrenia (Full-training AUC = 0.97 and 5-fold cross-validation AUC = 0.75). We identified several primary MPAs, such as the mouth region, high palate, furrowed tongue, skull height and mouth width. Quantitative MPA analysis indicated that the higher skull height and the narrower mouth width, the higher the risk of schizophrenia. In the interaction, we further identified that skull height and mouth width, furrowed tongue and skull height, high palate and skull height, and high palate and furrowed tongue, showed significant two-item interactions with schizophrenia. A weak three-item interaction was found between high palate, skull height, and mouth width. In conclusion, we found that the two machine learning methods showed good predictive ability in assessing the risk of schizophrenia using the primary and interaction effects of MPAs.

A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches.

The open radio access network (RAN) aims to bring openness and intelligence to the traditional closed and proprietary RAN technology and offer flexibility, performance improvement, and cost-efficiency in the RAN's deployment and operation. This paper provides a comprehensive survey of the Open RAN development. We briefly summarize the RAN evolution history and the state-of-the-art technologies applied to Open RAN. The Open RAN-related projects, activities, and standardization is then discussed. We then summarize the challenges and future research directions required to support the Open RAN. Finally, we discuss some solutions to tackle these issues from the open source perspective.

Increased expression of SSEA-4 on TKI-resistant non-small cell lung cancer with EGFR-T790M mutation.

Non-small cell lung cancer (NSCLC), a major life-threatening disease accounting for 85% of all lung cancer cases, has been treated with tyrosine kinase inhibitors (TKIs), but often resulted in drug resistance, and approximately 60% of TKI-resistant cases are due to acquired secondary (epithelial growth factor receptor) EGFR-T790M mutation. To identify alternative targets for TKI-resistant NSCLC with EGFR-T790M mutation, we found that the three globo-series glycosphingolipids are increasingly expressed on this type of NSCLC cell lines, and among them, the increase of stage-specific embryonic antigen-4 (SSEA-4) expression is the most significant. Compared to TKI-sensitive cell lines, SSEA-4 and the key enzyme ß3GalT5 responsible for the synthesis of SSEA3 are more expressed in TKI-resistant NSCLC cell lines with EGFR-T790M mutation, and the expression levels strongly correlate with poor survival in patients with EGFR mutation. In addition, we demonstrated that a SSEA-4 targeted monoclonal antibody, especially the homogeneous glycoform with well-defined Fc glycan designed to improve effective functions, is highly effective against this subpopulation of NSCLC in cell-based and animal studies. These findings provide a direction for the prediction of tumor recurrence and treatment of TKI-resistant NSCLC with EGFR-T790M mutation.

A multistate platform model for time-to-event endpoints in oncology clinical trials.

A multistate platform model was developed to describe time-to-event (TTE) endpoints in an oncology trial through the following states: initial, tumor response (TR), progressive disease (PD), overall survival (OS) event (death), censor to the last evaluable tumor assessment (progression-free survival [PFS] censor), and censor to study end (OS censor), using an ordinary differential equation framework. Two types of piecewise functions were used to describe the hazards for different events. Piecewise surge functions were used for events that require tumor assessments at the scheduled study visit times (TR, PD, and PFS censor). Piecewise constant functions were used to describe hazards for events that occur evenly throughout the study (OS event and OS censor). The multistate TTE model was applied to describe TTE endpoints from a published phase III study. The piecewise surge functions well-described the observed surges of hazards/events for TR, PD, PFS, and OS occurring near scheduled tumor assessments and showed good agreement with all Kaplan-Meier curves. With the flexibility of piecewise hazard functions, the model was able to evaluate covariate effects in a time-variant fashion to better understand the temporal patterns of disease prognosis through different disease states. This model can be applied to advance the field of oncology trial design and optimization by: (1) enabling robust estimations of baseline hazards and covariate effects for multiple TTE endpoints, (2) providing a platform model for understanding the composition and correlations between different TTE endpoints, and (3) facilitating oncology trial design optimization through clinical trial simulations.

Trends in the Development of Antibody-Drug Conjugates for Cancer Therapy.

In cancer treatment, the first-generation, cytotoxic drugs, though effective against cancer cells, also harmed healthy ones. The second-generation targeted cancer cells precisely to inhibit their growth. Enter the third-generation, consisting of immuno-oncology drugs, designed to combat drug resistance and bolster the immune system's defenses. These advanced therapies operate by obstructing the uncontrolled growth and spread of cancer cells through the body, ultimately eliminating them effectively. Within the arsenal of cancer treatment, monoclonal antibodies offer several advantages, including inducing cancer cell apoptosis, precise targeting, prolonged presence in the body, and minimal side effects. A recent development in cancer therapy is Antibody-Drug Conjugates (ADCs), initially developed in the mid-20th century. The second generation of ADCs addressed this issue through innovative antibody modification techniques, such as DAR regulation, amino acid substitutions, incorporation of non-natural amino acids, and enzymatic drug attachment. Currently, a third generation of ADCs is in development. This study presents an overview of 12 available ADCs, reviews 71 recent research papers, and analyzes 128 clinical trial reports. The overarching objective is to gain insights into the prevailing trends in ADC research and development, with a particular focus on emerging frontiers like potential targets, linkers, and drug payloads within the realm of cancer treatment.

Clinical outcomes of nirmatrelvir-ritonavir use in pregnant women during the Omicron wave of the coronavirus disease 2019 pandemic.

BACKGROUND: Paxlovid is an oral drug composed of nirmatrelvir and ritonavir that has been demonstrated to be effective in decreasing the risk of severe coronavirus disease 2019 (COVID-19). Here, we report the use of paxlovid in pregnant women with COVID-19. METHODS: Pregnant women attending a tertiary referral hospital in Taiwan from 29 April to 30 July 2022 were enrolled in the study. We compared baseline characteristics, clinical manifestations, and adverse events between paxlovid-treated women and those without paxlovid use. Maternal and neonatal outcomes were analysed in women who delivered during the study period. RESULTS: A total of 30 paxlovid-treated pregnant women and 55 women without paxlovid use were included in the analysis. The mean duration of COVID-19-associated symptoms in the paxlovid-treated women was shorter than that in the control group (10.10 days versus 15.59 days, p = 0.04). No severe adverse events due to paxlovid use were observed. Dysgeusia and diarrhoea were the most common adverse effects. Thirteen paxlovid-treated and 28 untreated women delivered during the study period. More pregnant women in the paxlovid group who delivered during the study period underwent caesarean delivery compared to the group without antiviral treatment (10 of 13 [76.92%] versus 12 of 28 [42.86%], p = 0.042), and insignificantly more newborns were born small for gestational age in the paxlovid group compared to the control group (3 of 13 [23.08%] versus 1 of 28 [3.57%], p = 0.086). CONCLUSION: Our study showed that paxlovid was effective and safe for pregnant women during the Omicron wave of the COVID-19 pandemic. A higher proportion of caesarean delivery rates was observed among paxlovid-treated women. Long-term follow-up of pregnant women exposed to paxlovid and their offspring is needed.

Effect of atosiban on in vitro fertilization pregnancy outcome among women with endometriosis in presence or absence of adenomyosis.

OBJECTIVE: This study aimed to assess the effect of atosiban on in vitro fertilization (IVF) pregnancy outcome among women with both endometriosis and adenomyosis, and compared it to that of patients with endometriosis but without adenomyosis and that of patients with tubal factor only. MATERIALS AND METHODS: 106 infertile women (176 embryo transfers) from a medical center in Taiwan were included in the analysis, where 34 (54), 34 (66), and 38 (56) cases (embryo transfers) were endometriosis without adenomyosis, endometriosis with adenomyosis, and tubal infertility factor only, respectively. Adenomyosis morphologies were classified using an ultrasound-based classification system. The logistic generalized estimating equation model was used to analyze the association between atosiban use and pregnancy outcomes. RESULTS: The crude pregnancy rates for the endometriosis-only group were significantly higher than those for the endometriosis + adenomyosis group (i.e., biochemical pregnancy: 50.0% versus 29.7%, p = 0.041; ongoing pregnancy: 35.2% versus 16.9%, p = 0.038). Significantly higher chances of biochemical pregnancy and ongoing pregnancy among endometriosis patients without adenomyosis versus those with both endometriosis and adenomyosis were found (odds ratios [95% confidence intervals]: 2.981 [1.307, 6.803]; p = 0.009, 2.694 [1.151, 6.304]; p = 0.022). A significant positive association between atosiban use and biochemical pregnancy existed among endometriosis cases without adenomyosis (a 2.43-fold [1.01, 5.89] increase in successful pregnancy; p<0.05), but not for the other groups. CONCLUSIONS: Poor pregnancy outcomes among adenomyosis-affected women were confirmed. The use of atosiban significantly enhanced IVF pregnancy among endometriosis patients without adenomyosis.

Polymorphisms within the Tumor Necrosis Factor-Alpha Gene Is Associated with Preeclampsia in Taiwanese Han Populations.

Preeclampsia (PE) occurs in women pregnant for more than 20 weeks with de novo hypertension and proteinuria, and is a devastating disease in maternal-fetal medicine. Cytokine tumor necrosis factor (TNF)-α may play a key role in the pathogenesis of PE. We conducted this study to investigate the regulatory regions of the TNF genes, by investigating two promoter polymorphisms, TNFA-308G/A (rs1800629) and -238G/A (rs361525), known to influence TNF expression, and their relationship to PE. An observational, monocentric, case-control study was conducted. We retrospectively collected 74 cases of severe PE and 119 pregnant women without PE as control. Polymerase chain reaction (PCR) was carried out for allele analysis. Higher A allele in women with PE was found in rs1800629 but not rs361525. In this study, we first found that polymorphism at the position -308, but not -238, in the promoter region of the TNF-α gene can contribute to severe PE in Taiwanese Han populations. The results of our study are totally different to previous Iranian studies, but have some similarity to a previous UK study. Further studies are required to confirm the roles of rs1800629 and rs361525 in PE with circulating TNF-α in PE.

Estimation of time to progression and post progression survival using joint modeling of summary level OS and PFS data with an ordinary differential equation model.

measures such as progression-free survival (PFS) and overall survival (OS) are commonly reported in literature for oncology trials, while time to progression (TTP) and post progression survival (PPS) are not usually reported. A time-variant transition hazard model was developed using an ordinary differential equation (ODE) model to estimate TTP and PPS from summary level PFS and OS. The model was applied to published data from immune checkpoint inhibitor trials for non-small cell lung cancer (NSCLC) in a meta-analysis framework. This model-based method was able to robustly estimate TTP and PPS from summary level OS and PFS data, provided a quantitative approach for understanding the patterns of disease progression across different treatments through the time-variant disease progression rate function, and provided a summary of how different treatments affect TTP and PPS. The proposed method can be generalized to characterize and quantify multiple time-to-event endpoints jointly in oncology trials and improve our understanding of disease prognostics for different treatments.

A case report of appendiceal adenocarcinoma without gastrointestinal evidence mimicking primary ovarian cancer.

Metastatic tumours to the ovary comprise 10-25% of ovarian malignancies and may originate from various primary sites. Here, the case of a 49-year-old female patient who presented with periumbilical nodules and abdominal bloating is reported. She was found to have bilateral ovarian tumours with peritoneal carcinomatosis and ascites. Primary ovarian cancer was suspected while no contributory gastrointestinal lesion was detected by imaging studies and endoscopic examinations. Three cycles of neoadjuvant chemotherapy were administered, followed by interval debulking surgery. Appendiceal cancer was highly suspected based on analysis of a frozen section obtained during surgical debulking. Following the pathology investigation, the patient was finally diagnosed with primary appendiceal adenocarcinoma. She underwent chemotherapy comprising irinotecan and fluorouracil. Due to disease progression despite several chemotherapy regimens, the patient declined further treatment and was lost to follow-up 1 year after the debulking surgery. Metastatic tumours to the ovary may mimic primary ovarian cancers and often present with nonspecific manifestations. Therefore, meticulous exploration of the primary site is warranted if the diagnosis is clinically suspicious.

SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation.

Precipitation intensity estimation is a critical issue in the analysis of weather conditions. Most existing approaches focus on building complex models to extract rain streaks. However, an efficient approach to estimate the precipitation intensity from surveillance cameras is still challenging. This study proposes a convolutional neural network known as the signal filtering convolutional neural network (SF-CNN) to handle precipitation intensity using surveillance-based images. The SF-CNN has two main blocks, the signal filtering block (SF block) and the gradually decreasing dimension block (GDD block), to extract features for the precipitation intensity estimation. The SF block with the filtering operation is constructed in different parts of the SF-CNN to remove the noise from the features containing rain streak information. The GDD block continuously takes the pair of the convolutional operation with the activation function to reduce the dimension of features. Our main contributions are (1) an SF block considering the signal filtering process and effectively removing the useless signals and (2) a procedure of gradually decreasing the dimension of the feature able to learn and reserve the information of features. Experiments on the self-collected dataset, consisting of 9394 raining images with six precipitation intensity levels, demonstrate the proposed approach's effectiveness against the popular convolutional neural networks. To the best of our knowledge, the self-collected dataset is the largest dataset for monitoring infrared images of precipitation intensity.

Design and synthesis of fluorinated peptides for analysis of fluorous effects on the interconversion of polyproline helices.

The unique interaction between fluorine atoms has been exploited to alter protein structures and to develop synthetic and analytical applications. To expand such fluorous interaction for novel applications, polyproline peptides represent an excellent molecular nanoscaffold for controlling the presentation of perfluoroalkyl groups on their unique secondary structure. We develop approaches to synthesis fluorinated peptides to systematically investigate how the number, location and types of the fluorous groups on polyproline affect the conformation by monitoring the transition between the two major polyproline structures PPI and PPII. This work provides valuable information on how fluorous interaction affects the peptide structure and also benefits the design of functional fluorous molecules.

A Novel Game-Based Intelligent Test for Detecting Elderly Cognitive Function Impairment.

PURPOSES: This research explores the game-based intelligent test (GBIT), predicts the possibilities of Mini-Mental State Examination (MMSE) scores and the risk of cognitive impairment, and then verifies GBIT as one of the reliable and valid cognitive assessment tools. METHODS: This study recruited 117 elderly subjects in Taiwan (average age is 79.92 ± 8.68, average height is 156.91 ± 8.01, average weight is 59.14 ± 9.67, and average MMSE score is 23.33 ± 6.16). A multiple regression model was used to analyze the GBIT parameters of the elderly's reaction, attention, coordination, and memory to predict their MMSE performance. The binary logistic regression was then utilized to predict their risk of cognitive impairment. The statistical significance level was set as α = 0.05. RESULTS: Multiple regression analysis showed that gender, the correct number of reactions, and the correct number of memory have a significantly positive predictive power on MMSE of the elderly (F = 37.60, R 2 = 0.69, and p < 0.05). Binary logistic regression analysis noted that the correct average number of reactions falls by one question, and the ratio of cognitive dysfunction risk increases 1.09 times (p < 0.05); the correct average number of memory drops by one question, the ratio of cognitive dysfunction risk increases 3.76 times (p < 0.05), and the overall model predictive power is 88.20% (sensitivity: 84.00%; specificity: 92.30%). CONCLUSIONS: This study verifies that GBIT is reliable and can effectively predict the cognitive function and risk of cognitive impairment in the elderly. Therefore, GBIT can be used as one of the feasible tools for evaluating older people's cognitive function.

Homogeneous antibody and CAR-T cells with improved effector functions targeting SSEA-4 glycan on pancreatic cancer.

Pancreatic cancer is usually asymptomatic in the early stages; the 5-y survival rate is around 9%; and there is a lack of effective treatment. Here we show that SSEA-4 is more expressed in all pancreatic cancer cell lines examined but not detectable in normal pancreatic cells; and high expression of SSEA-4 or the key enzymes B3GALT5 + ST3GAL2 associated with SSEA-4 biosynthesis significantly lowers the overall survival rate. To evaluate potential new treatments for pancreatic cancer, homogeneous antibodies with a well-defined Fc glycan for optimal effector functions and CAR-T cells with scFv construct designed to target SSEA-4 were shown highly effective against pancreatic cancer in vitro and in vivo. This was further supported by the finding that a subpopulation of natural killer (NK) cells isolated by the homogeneous antibody exhibited enhancement in cancer-cell killing activity compared to the unseparated NK cells. These results indicate that targeting SSEA-4 by homologous antibodies or CAR-T strategies can effectively inhibit cancer growth, suggesting SSEA-4 as a potential immunotherapy target for treating pancreatic disease.

Dihydroceramide desaturase promotes the formation of intraluminal vesicles and inhibits autophagy to increase exosome production.

Exosomes are important for cell-cell communication. Deficiencies in the human dihydroceramide desaturase gene, DEGS1, increase the dihydroceramide-to-ceramide ratio and cause hypomyelinating leukodystrophy. However, the disease mechanism remains unknown. Here, we developed an in vivo assay with spatially controlled expression of exosome markers in Drosophila eye imaginal discs and showed that the level and activity of the DEGS1 ortholog, Ifc, correlated with exosome production. Knocking out ifc decreased the density of the exosome precursor intraluminal vesicles (ILVs) in the multivesicular endosomes (MVEs) and reduced the number of exosomes released. While ifc overexpression and autophagy inhibition both enhanced exosome production, combining the two had no additive effect. Moreover, DEGS1 activity was sufficient to drive ILV formation in vitro. Together, DEGS1/Ifc controls the dihydroceramide-to-ceramide ratio and enhances exosome secretion by promoting ILV formation and preventing the autophagic degradation of MVEs. These findings provide a potential cause for the neuropathy associated with DEGS1-deficient mutations.

A new immunochemical strategy for triple-negative breast cancer therapy.

Triple-negative breast cancer (TNBC) is a highly diverse group of malignant neoplasms which tend to have poor outcomes, and the development of new targets and strategies to treat these cancers is sorely needed. Antibody-drug conjugate (ADC) therapy has been shown to be a promising targeted therapy for treating many cancers, but has only rarely been tried in patients with TNBC. A major reason the efficacy of ADC therapy in the setting of TNBC has not been more fully investigated is the lack of appropriate target molecules. In this work we were able to identify an effective TNBC target for use in immunotherapy. We were guided by our previous observation that in some breast cancer patients the protein tropomyosin receptor kinase B cell surface protein (TrkB) had become immunogenic, suggesting that it was somehow sufficiently chemically different enough (presumably by mutation) to escaped immune tolerance. We postulated that this difference might well offer a means for selective targeting by antibodies. We engineered site-specific ADCs using a dual variable domain (DVD) format which combines anti-TrkB antibody with the h38C2 catalytic antibody. This format enables rapid, one-step, and homogeneous conjugation of ß-lactam-derivatized drugs. Following conjugation to ß-lactam-derivatized monomethyl auristatin F, the TrkB-targeting DVD-ADCs showed potency against multiple breast cancer cell lines, including TNBC cell lines. In addition, our isolation of antibody that specifically recognized the breast cancer-associated mutant form of TrkB, but not the wild type TrkB, indicates the possibility of further refining the selectivity of anti-TrkB DVD-ADCs, which should enhance their therapeutic index. These results confirmed our supposition that TrkB is a potential target for immunotherapy for TNBC, as well as for other cancers with mutated cell surface proteins.