Pediatric Traumatic Brain Injury Outcomes and Disparities During the COVID-19 Pandemic.

BACKGROUND: To study the impact of the COVID-19 pandemic on traumatic brain injury (TBI) patient demographic, clinical and trauma related characteristics, and outcomes. METHODS: Retrospective chart review was conducted on pediatric TBI patients admitted to a Level I Pediatric Trauma Center between January 2015 and June 2022. The pre-COVID era was defined as January 1, 2015, through March 12, 2020. The COVID-19 era was defined as March 13, 2020, through June 30, 2022. Bivariate analysis and logistic regression were performed. RESULTS: Four hundred-thirty patients were treated for pediatric TBI in the pre-COVID-19 period, and 166 patients during COVID-19. In bivariate analyses, the racial/ethnic makeup, age, and sex varied significantly across the two time periods (p < 0.05). Unwitnessed TBI events increased during the COVID-19 era. Logistic regression analyses also demonstrated significantly increased odds of death, severe disability, or vegetative state during COVID-19 (AOR 7.23; 95 % CI 1.43, 36.41). CONCLUSION: During the COVID-19 pandemic, patients admitted with pediatric TBI had significantly different demographics with regards to age, sex, and race/ethnicity when compared to patients prior to the pandemic. There was an increase in unwitnessed events. In the COVID period, patients had a higher odds ratio of severe morbidity and mortality despite adjustment for confounding factors. LEVEL OF EVIDENCE AND STUDY TYPE: Level II, Prognosis.

Construction of Candida albicans Adhesin-Exposed Synthetic Cells for Preventing Systemic Fungal Infection.

The development of efficient fungal vaccines is urgent for preventing life-threatening systemic fungal infections. In this study, we prepared a synthetic, cell-based fungal vaccine for preventing systemic fungal infections using synthetic biology techniques. The synthetic cell EmEAP1 was constructed by transforming the Escherichia coli chassis using a de novo synthetic fragment encoding the protein mChEap1 that was composed of the E. coli OmpA peptide, the fluorescence protein mCherry, the Candida albicans adhesin Eap1, and the C-terminally transmembrane region. The EmEAP1 cells highly exposed the mChEap1 on the cell surface under IPTG induction. The fungal vaccine was then prepared by mixing the EmEAP1 cells with aluminum hydroxide gel and CpG. Fluorescence quantification revealed that the fungal vaccine was stable even after 112 days of storage. After immunization in mice, the vaccine resided in the lymph nodes, inducing the recruitment of CD11c+ dendritic cells. Moreover, the vaccine strongly activated the CD4+ T splenocytes and elicited high levels of anti-Eap1 IgG. By the prime-boost immunization, the vaccine prolonged the survival time of the mice infected by the C. albicans cells and attenuated fungal colonization together with inflammation in the kidneys. This study sheds light on the development of synthetic biology-based fungal vaccines for the prevention of life-threatening fungal infections.

Regulation of cadmium-induced biofilm formation by artificial polysaccharide-binding proteins for enhanced phytoremediation.

Phytoremediation is an economic way to attenuate soil heavy metal pollution, but is frequently limited by its low pollutant-removing efficiency. Recently, we revealed the close relation between polysaccharide-based biofilm formation and cadmium removal. In this study, for improving the phytoremediation efficiency, an artificial polysaccharide-binding protein was designed by synthetic biology techniques to regulate biofilm formation. The artificial protein Syn contained two polysaccharide-binding domains from the Ruminococcus flavefaciens CttA and the Clostridium cellulolyticum CipC, preferentially binding polysaccharides exposed on both cadmium-treated bacteria and plant roots. Under cadmium stress, Syn remarkably promoted bacterial polysaccharide production from 99 mg/L to 237 mg/L, leading to 1.23-fold higher biofilm biomass. During treatment of the remediation plants with exogenous cadmium-capturing bacteria, Syn improved root biofilm formation, with the root surface polysaccharide contents increasing by 79%, and the Log10 CFU/g root increasing from 7.01 to 7.80. Meanwhile, Syn remodeled the rhizosphere microbiome, especially increasing the abundance of the bacterial groups involved in biofilm formation and stress tolerance, e.g., Pseudomonas, Enterobacter, etc. Consequently, Syn promoted plant cadmium adsorption, with the cadmium-removing efficiency increasing from 17.2% to 33.8%. This study sheds light on synthetic biology-based regulation of biofilm formation for enhanced phytoremediation.

Analysis of the Potassium-Solubilizing Priestia megaterium Strain NK851 and Its Potassium Feldspar-Binding Proteins.

Potassium-solubilizing bacteria are an important microbial group that play a critical role in releasing mineral potassium from potassium-containing minerals, e.g., potassium feldspar. Their application may reduce eutrophication caused by overused potassium fertilizers and facilitate plants to utilize environmental potassium. In this study, a high-efficiency potassium-solubilizing bacterium, named NK851, was isolated from the Astragalus sinicus rhizosphere soil. This bacterium can grow in the medium with potassium feldspar as the sole potassium source, releasing 157 mg/L and 222 mg/L potassium after 3 days and 5 days of incubation, respectively. 16S rDNA sequencing and cluster analysis showed that this strain belongs to Priestia megaterium. Genome sequencing further revealed that this strain has a genome length of 5,305,142 bp, encoding 5473 genes. Among them, abundant genes are related to potassium decomposition and utilization, e.g., the genes involved in adherence to mineral potassium, potassium release, and intracellular trafficking. Moreover, the strong potassium-releasing capacity of NK851 is not attributed to the acidic pH but is attributed to the extracellular potassium feldspar-binding proteins, such as the elongation factor TU and the enolase that contains potassium feldspar-binding cavities. This study provides new information for exploration of the bacterium-mediated potassium solubilization mechanisms.

Magnetic nanoparticle-assisted colonization of synthetic bacteria on plant roots for improved phytoremediation of heavy metals.

Phytoremediation is a facile strategy to remove environmental heavy metals by using metal-accumulating plants from the rhizosphere environment. However, its efficiency is frequently compromised by the weak activity of rhizosphere microbiomes. This study developed a magnetic nanoparticle-assisted root colonization technique of synthetic functional bacteria to regulate rhizosphere microbiome composition for enhanced phytoremediation of heavy metals. The iron oxide magnetic nanoparticles with the size of 15-20 nm were synthesized and grafted by chitosan, a natural bacterium-binding polymer. The synthetic Escherichia coli SynEc2, which highly exposed an artificial heavy metal-capturing protein, was then introduced with the magnetic nanoparticles to bind the Eichhornia crassipes plants. Confocal microscopy, scanning electron microscopy, and microbiome analysis revealed that the grafted magnetic nanoparticles strongly promoted colonization of the synthetic bacteria on the plant roots, leading to remarkable change of rhizosphere microbiome composition, with the increase in the abundance of Enterobacteriaceae, Moraxellaceae, and Sphingomonadaceae. Histological staining and biochemical analysis further showed that the combination of SynEc2 and the magnetic nanoparticles protected the plants from heavy metal-induced tissue damage, and increased plant weights from 29 g to 40 g. Consequently, the plants with the assistance of synthetic bacteria and the magnetic nanoparticles in combination exhibited much higher heavy metal-removing capacity than the plants treated by the synthetic bacteria or the magnetic nanoparticles alone, leading to the decrease in the heavy metal levels from 3 mg/L to 0.128 mg/L for cadmium, and to 0.032 mg/L for lead. This study provided a novel strategy to remodel rhizosphere microbiome of metal-accumulating plants by integrating synthetic microbes and nanomaterials for improving the efficiency of phytoremediation.

A modified oblique lumbar interbody fusion: A better way to establish an exposure under direct microscopic vision.

Study design: This is a retrospective study. Objective: To demonstrate a modified oblique lumbar interbody fusion (OILF) technique for L1-L5. Methods: The modified technique splits anterior portion of psoas belly to access the oblique corridor (OC) anteroinferior to psoas, minimizing psoas manipulation and retraction and avoiding nerve injury while offering excellent microscopic visualization. Psoas weakness and neurovascular complication rates in patients treated with traditional OLIF (T-OLIF) or anteroinferior psoas OLIF (AP-OLIF) were retrospectively reviewed. Clinical outcomes were also reviewed. Results: A total of 162 cases treated with T-OLIF (n = 73) and AP-OLIF (n = 89) for degenerative lumbar disease were included. The mean operative time and blood loss were less with AP-OLIF (P < 0.01). Approach related complications were 14 (19.1%) with T-OLIF and 4 (4.5%) with AP-OLIF. Postoperative visual analog scale (VAS) and Oswestry Disability Index (ODI) scores improved in both T-OIF and AP-OIF groups (P < 0.01). Conclusion: The modified OLIF technique (AP-OLIF) is characterized by an easy exposure of the lumbar spine under direct microscopic vision, resulting in less psoas weakness and neurovascular injury.

A Young Child With Recurrent Pneumonia and Hemoptysis During the COVID-19 Pandemic.

CASE PRESENTATION: In July 2020, a previously healthy 6-year-old boy was evaluated in a pulmonary clinic in New York after two episodes of pneumonia in the previous 3 months. For each episode, the patient presented with cough, fever, and hemoptysis, all of which resolved with antibiotic therapy and supportive care. The patient never experienced dyspnea during these episodes of pneumonia. He was asymptomatic at the current visit. The patient had no history of travel, sick contacts, asthma, or bleeding disorders.

Cornel Iridoid Glycoside Protects Against STAT1-Dependent Synapse and Memory Deficits by Increasing N-Methyl-D-aspartate Receptor Expression in a Tau Transgenic Mice.

P301S transgenic mice are an animal model of tauopathy and Alzheimer's disease (AD), exhibiting tau pathology and synaptic dysfunction. Cornel iridoid glycoside (CIG) is an active ingredient extracted from Cornus officinalis, a traditional Chinese herb. In the present study, the purpose was to investigate the effects and mechanisms of CIG on tau pathology and synaptic dysfunction using P301S transgenic mice. The results showed that intragastric administration of CIG for 3.5 months improved cognitive impairments and the survival rate of P301S mice. Electrophysiological recordings and transmission electron microscopy study showed that CIG improved synaptic plasticity and increased the ultrastructure and number of synapse. Moreover, CIG increased the expression levels of N-methyl-D-aspartate receptors (NMDAR) subunits GluN1, GluN2A, and GluN2B, and α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) subunit GluA1. We inferred that the major mechanism of CIG involving in the regulation of synaptic dysfunctions was inhibiting the activation of Janus kinase-2 (JAK2)/signal transducer and activator of transcription 1 (STAT1) signaling pathway and alleviating STAT1-induced suppression of NMDAR expressions. Based on our findings, we thought CIG might be a promising candidate for the therapy of tauopathy such as AD.

Agitation Following Severe Traumatic Brain Injury Is a Clinical Sign of Recovery of Consciousness.

Objective: Severe traumatic brain injury (sTBI) often results in disorders of consciousness. Patients emerging from coma frequently exhibit aberrant behaviors such as agitation. These non-purposeful combative behaviors can interfere with medical care. Interestingly, agitation is associated with arousal and is often among the first signs of neurological recovery. A better understanding of these behaviors may shed light on the mechanisms driving the return of consciousness in sTBI patients. This study aims to investigate the association between posttraumatic agitation and the recovery of consciousness. Methods: A retrospective chart review was conducted in 530 adult patients (29.1% female) admitted to Stony Brook University Hospital between January 2011 and December 2019 with a diagnosis of sTBI and Glasgow Coma Scale (GCS) ≤8. Agitation was defined as a Richmond Agitation Sedation Scale (RASS) > +1, or any documentation of equivalently combative and violent behaviors in daily clinical notes. The ability to follow verbal commands was used to define the recovery of consciousness and was assessed daily. Results: Of 530 total sTBI patients, 308 (58.1%) survived. Agitation was present in 169 of all patients and 162 (52.6%) of surviving patients. A total of 273 patients followed commands, and 159 of them developed agitation. Forty patients developed agitation on hospital arrival whereas 119 developed agitation later during their hospital course. Presence of in-hospital agitation positively correlated with command-following (r = 0.315, p < 0.001). The time to develop agitation and time to follow commands showed positive correlation (r = 0.485, p < 0.001). These two events occurred within 3 days in 54 (44.6%) patients, within 7 days in 81 (67.8%) patients, and within 14 days in 96 (80.2%) patients. In 71 (59.7%) patients, agitation developed before command-following; in 36 (30.2%) patients, agitation developed after command-following; in 12 (10.1%) patients, agitation developed on the same day as command-following. Conclusion: Posttraumatic agitation in comatose patients following sTBI is temporally associated with the recovery of consciousness. This behavior indicates the potential for recovery of higher neurological functioning. Further studies are required to identify neural correlates of posttraumatic agitation and recovery of consciousness after sTBI.

Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study.

Objectives: To characterize the temporal characteristics of clinical variables with time lock to mortality and build a predictive model of mortality associated with COVID-19 using clinical variables. Design: Retrospective cohort study of the temporal characteristics of clinical variables with time lock to mortality. Setting: Stony Brook University Hospital (New York) and Tongji Hospital. Patients: Patients with confirmed positive for severe acute respiratory syndrome coronavirus-2 using polymerase chain reaction testing. Patients from the Stony Brook University Hospital data were used for training (80%, N = 1,002) and testing (20%, N = 250), and 375 patients from the Tongji Hospital (Wuhan, China) data were used for testing. Intervention: None. Measurements and Main Results: Longitudinal clinical variables were analyzed as a function of days from outcome with time-lock-to-day of death (non-survivors) or discharge (survivors). A predictive model using the significant earliest predictors was constructed. Performance was evaluated using receiver operating characteristics area under the curve (AUC). The predictive model found lactate dehydrogenase, lymphocytes, procalcitonin, D-dimer, C-reactive protein, respiratory rate, and white-blood cells to be early predictors of mortality. The AUC for the zero to 9 days prior to outcome were: 0.99, 0.96, 0.94, 0.90, 0.82, 0.75, 0.73, 0.77, 0.79, and 0.73, respectively (Stony Brook Hospital), and 1.0, 0.86, 0.88, 0.96, 0.91, 0.62, 0.67, 0.50, 0.63, and 0.57, respectively (Tongji Hospital). In comparison, prediction performance using hospital admission data was poor (AUC = 0.59). Temporal fluctuations of most clinical variables, indicative of physiological and biochemical instability, were markedly higher in non-survivors compared to survivors (p < 0.001). Conclusion: This study identified several clinical markers that demonstrated a temporal progression associated with mortality. These variables accurately predicted death within a few days prior to outcome, which provides objective indication that closer monitoring and interventions may be needed to prevent deterioration.

Machining learning predicts the need for escalated care and mortality in COVID-19 patients from clinical variables.

Objective: This study aimed to develop a machine learning algorithm to identify key clinical measures to triage patients more effectively to general admission versus intensive care unit (ICU) admission and to predict mortality in COVID-19 pandemic. Materials and methods: This retrospective study consisted of 1874 persons-under-investigation for COVID-19 between February 7, 2020, and May 27, 2020 at Stony Brook University Hospital, New York. Two primary outcomes were ICU admission and mortality compared to COVID-19 positive patients in general hospital admission. Demographic, vitals, symptoms, imaging findings, comorbidities, and laboratory tests at presentation were collected. Predictions of mortality and ICU admission were made using machine learning with 80% training and 20% testing. Performance was evaluated using receiver operating characteristic (ROC) area under the curve (AUC). Results: A total of 635 patients were included in the analysis (age 60±11, 40.2% female). The top 6 mortality predictors were age, procalcitonin, C-creative protein, lactate dehydrogenase, D-dimer and lymphocytes. The top 6 ICU admission predictors are procalcitonin, lactate dehydrogenase, C-creative protein, pulse oxygen saturation, temperature and ferritin. The best machine learning algorithms predicted mortality with 89% AUC and ICU admission with 79% AUC. Conclusion: This study identifies key independent clinical parameters that predict ICU admission and mortality associated with COVID-19 infection. The predictive model is practical, readily enhanced and retrained using additional data. This approach has immediate translation and may prove useful for frontline physicians in clinical decision making under time-sensitive and resource-constrained environment.

Neuroinflammatory changes of the normal brain tissue in cured mice following combined radiation and anti-PD-1 blockade therapy for glioma.

The efficacy of combining radiation therapy with immune checkpoint inhibitor blockade to treat brain tumors is currently the subject of multiple investigations and holds significant therapeutic promise. However, the long-term effects of this combination therapy on the normal brain tissue are unknown. Here, we examined mice that were intracranially implanted with murine glioma cell line and became long-term survivors after treatment with a combination of 10 Gy cranial irradiation (RT) and anti-PD-1 checkpoint blockade (aPD-1). Post-mortem analysis of the cerebral hemisphere contralateral to tumor implantation showed complete abolishment of hippocampal neurogenesis, but neural stem cells were well preserved in subventricular zone. In addition, we observed a drastic reduction in the number of mature oligodendrocytes in the subcortical white matter. Importantly, this observation was evident specifically in the combined (RT + aPD-1) treatment group but not in the single treatment arm of either RT alone or aPD-1 alone. Elimination of microglia with a small molecule inhibitor of colony stimulated factor-1 receptor (PLX5622) prevented the loss of mature oligodendrocytes. These results identify for the first time a unique pattern of normal tissue changes in the brain secondary to combination treatment with radiotherapy and immunotherapy. The results also suggest a role for microglia as key mediators of the adverse treatment effect.

Cardiac arrest after severe traumatic brain injury can be survivable with good outcomes.

BACKGROUND: Resuscitation for traumatic cardiac arrest (TCA) in patients with severe traumatic brain injury (sTBI) has historically been considered futile. There is little information on the characteristics and outcomes of these patients to guide intervention and prognosis. The purpose of the current study is to report the clinical characteristics, survival, and long-term neurological outcomes in patients who experienced TCA after sTBI and analyze the factors contributing to survival. METHODS: A retrospective review identified 42 patients with TCA from a total of 402 patients with sTBI (Glasgow Coma Scale (GCS) score ≤8) who were admitted to Stony Brook University Hospital, a level I trauma center, from January 2011 to December 2018. Patient demographics, clinical characteristics, survival, and neurological functioning during hospitalization and at follow-up visits were collected. RESULTS: Of the 42 patients, the average age was 45 years and 21.4% were female. Eight patients survived the injury (19.0%) to discharge and seven survived with good neurological function. Admission GCS score and bilateral pupil reactivity were found to be significant indicators of survival. The mean GCS score was 5.3 in survivors and 3.2 in non-survivors (p=0.020). Age, Injury Severity Score, or cardiac rhythm was not associated with survival. Frequent neuroimaging findings included subarachnoid hemorrhage, subdural hematoma, and diffuse axonal injury. DISCUSSION: TCA after sTBI is survivable and seven out of eight patients in our study recovered with good neurological function. GCS score and pupil reactivity are the best indicators of survival. Our results suggest that due to the possibility of recovery, resuscitation and neurosurgical care should not be withheld from this patient population. LEVEL OF EVIDENCE: Level IV, therapeutic/care management.

Intradural view of the spinal cord and dura after three-column osteotomy: illustrative case.

BACKGROUND: A three-column osteotomy results in dural buckling, which may appear concerning upon intraoperative visualization because it may appear that the neural elements may also be buckled. The authors presented an intraoperative view after intentional durotomy of the neural elements and the relaxed state of the dura after three-column osteotomy. OBSERVATIONS: A 52-year-old woman with adult tethered cord syndrome and previous untethering presented with worsening leg pain and stiffness, urinary incontinence, and unbalanced gait. Magnetic resonance imaging demonstrated an arachnoid web at T6 and spinal cord tethering. Spinal column shortening via three-column osteotomy was performed with concomitant intradural excision of the arachnoid web. Dural buckling was observed intraoperatively after spinal column shortening. After the durotomy, the spinal cord was visualized without kinking or buckling. LESSONS: Dural buckling after spinal column shortening of 15 mm via three-column osteotomy at T6 did not result in concomitant buckling of the underlying neural elements.

Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection.

BACKGROUND: The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. PURPOSE: The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. MATERIALS AND METHODS: Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis. RESULTS: For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively. CONCLUSION: AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.

Deep-learning artificial intelligence analysis of clinical variables predicts mortality in COVID-19 patients.

Objective: The large number of clinical variables associated with coronavirus disease 2019 (COVID-19) infection makes it challenging for frontline physicians to effectively triage COVID-19 patients during the pandemic. This study aimed to develop an efficient deep-learning artificial intelligence algorithm to identify top clinical variable predictors and derive a risk stratification score system to help clinicians triage COVID-19 patients. Methods: This retrospective study consisted of 181 hospitalized patients with confirmed COVID-19 infection from January 29, 2020 to March 21, 2020 from a major hospital in Wuhan, China. The primary outcome was mortality. Demographics, comorbidities, vital signs, symptoms, and laboratory tests were collected at initial presentation, totaling 78 clinical variables. A deep-learning algorithm and a risk stratification score system were developed to predict mortality. Data were split into 85% training and 15% testing. Prediction performance was compared with those using COVID-19 severity score, CURB-65 score, and pneumonia severity index (PSI). Results: Of the 181 COVID-19 patients, 39 expired and 142 survived. Five top predictors of mortality were D-dimer, O2 Index, neutrophil:lymphocyte ratio, C-reactive protein, and lactate dehydrogenase. The top 5 predictors and the resultant risk score yielded, respectively, an area under curve (AUC) of 0.968 (95% CI = 0.87-1.0) and 0.954 (95% CI = 0.80-0.99) for the testing dataset. Our models outperformed COVID-19 severity score (AUC = 0.756), CURB-65 score (AUC = 0.671), and PSI (AUC = 0.838). The mortality rates for our risk stratification scores (0-5) were 0%, 0%, 6.7%, 18.2%, 67.7%, and 83.3%, respectively. Conclusions: Deep-learning prediction model and the resultant risk stratification score may prove useful in clinical decisionmaking under time-sensitive and resource-constrained environment.

Prediction model and risk scores of ICU admission and mortality in COVID-19.

This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63-0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73-0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment.

Repolarized macrophages, induced by intermediate stereotactic dose radiotherapy and immune checkpoint blockade, contribute to long-term survival in glioma-bearing mice.

INTRODUCTION: Glioblastoma multiforme (GBM) is a deadly brain tumor with a short expected median survival, despite current standard-of-care treatment. We explored the combination of intermediate stereotactic dose radiation therapy and immune checkpoint inhibitor therapy as a novel treatment strategy for GBM. METHODS: Glioma xenograft-bearing mice were exposed to high dose brain-directed radiation (10 Gy single exposure) as well as mouse anti-PD-1 antibody. The tumor-bearing animals were randomized to four groups: no treatment, radiation alone, anti-PD-1 alone, and radiation + anti-PD-1. Survival was followed, and tumor growth was monitored using MRI. Immunohistochemistry, gene expression arrays, and flow cytometry were used to characterize the treatment-induced effects. Pharmacologic inhibitors of T-lymphocytes, bone marrow derived macrophages, and microglia were used to assess the respective roles of different immune populations in observed treatment effects. RESULTS: We found the combined treatment with high dose radiation and immunotherapy to be highly effective with a 75% complete pathologic response and dramatically improved survival outcomes. We found both CD8+ T-cells and macrophages to be necessary for the full effect of combined therapy, with T lymphocytes appearing to play a role early on and macrophages mediating a later phase of the combined treatment effect. Radiation treatment appeared to trigger macrophage repolarization, increasing M1/M2 ratio. CONCLUSIONS: These findings point to a novel immunologic mechanism underlying the interaction between radiotherapy and immunotherapy. They also provide the basis for clinical investigation of immunogenic dose radiation in combination with immune checkpoint blockade as a potential treatment approach for newly diagnosed high grade gliomas.

A Two-Sequence, Four-Period, Crossover, Replicate Study to Demonstrate Bioequivalence of Warfarin Sodium Tablet in Healthy Chinese Subjects Under Fasting and Fed Conditions.

Warfarin is a narrow therapeutic index anticoagulant drug, and several generic formulations have been approved worldwide. However, there has been no report evaluating the bioequivalence of warfarin sodium according to US Food and Drug Administration draft guidance. We designed a 2-sequence and 4-period crossover study to compare the pharmacokinetic profile and assess bioequivalence between the test warfarin sodium tablet and reference product Coumadin (2.5 mg) in 56 healthy Chinese subjects under fasting and fed conditions. The plasma concentration of warfarin was analyzed by a validated liquid chromatography-tandem mass spectrometry assay, and the reference-scaled procedure was used to determine bioequivalence for the pharmacokinetics parameters. The results showed that the point estimate of geometric mean ratios of Cmax and AUC0-t for warfarin were 103.21% and 99.31%, respectively, in the fasting condition and 100.62% and 98.98%, respectively, in the fed condition, and the 90% confidence intervals were all within the range of 90.00%-111.11%. The upper limit of the 90% confidence interval of estimated within-subject variation ratios of the test and reference products was 1.33 for Cmax and 2.22 for AUC0-t under the fasting condition and 1.68 for Cmax and 2.15 for AUC0-t under the fed condition. Overall, bioequivalence of the 2 warfarin sodium products was demonstrated.

Cornel Iridoid Glycoside Suppresses Hyperactivity Phenotype in rTg4510 Mice through Reducing Tau Pathology and Improving Synaptic Dysfunction.

rTg4510 mice are transgenic mice expressing P301L mutant tau and have been developed as an animal model of tauopathies including Alzheimer's disease (AD). Besides cognitive impairments, rTg4510 mice also show abnormal hyperactivity behavior. Cornel iridoid glycoside (CIG) is an active ingredient extracted from Cornus officinalis, a traditional Chinese herb. The purpose of the present study was to investigate the effects of CIG on the emotional disorders such as hyperactivity, and related mechanisms. The emotional hyperactivity was detected by locomotor activity test and Y maze test. Immunofluorescent and immunohistochemical analyses were conducted to measure neuron loss and phosphorylated tau. Western blotting was used to detect the expression of related proteins. The results showed that intragastric administration of CIG for 3 months decreased the hyperactivity phenotype, prevented neuronal loss, reduced tau hyperphosphorylation and aggregation in the amygdala of rTg4510 mice. Meanwhile, CIG alleviated the synaptic dysfunction by increasing the expression of N-methyl-D-aspartate receptors (NMDARs) subunits GluN1 and GluN2A and αamino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) subunits GluA1 and GluA2, and increased the level of phosphorylated Ca2+/calmodulin dependent protein kinase II α (p-CaMK IIα) in the brain of rTg4510 mice. In conclusion, CIG may have potential to treat the emotional disorders in tauopathies such as AD through reducing tau pathology and improving synaptic dysfunction.