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Cardiovascular disease (CVD), a type of circulatory system disease related to the lesions of the cardiovascular system, has become one of the main diseases that endanger human health. Currently, the clinical diagnosis of most CVDs relies on a combination of imaging technology and blood biochemical test. However, the existing technologies for diagnosis of CVDs still have limitations in terms of specificity, detection range, and cost. In order to break through the current bottleneck, microfluidic with the advantages of low cost, simple instruments and easy integration, has been developed to play an important role in the early prevention, diagnosis and treatment of CVDs. Here, we have reviewed the recent various applications of microfluidic in the clinical diagnosis and treatment of CVDs, including microfluidic devices for detecting CVD markers, the cardiovascular models based on microfluidic, and the microfluidic used for CVDs drug screening and delivery. In addition, we have briefly looked forward to the prospects and challenges of microfluidics in diagnosis and treatment of CVDs.

The nano-sperm is developed for ultrafast delivery of antisense DNA into cancer cells. Carried by silver nanoclusters at the “head”, antisense DNA as the cargo at the “tail” can be transported into cells rapidly, thereby inhibiting cell proliferation.

Rapid detection of pathogenic bacteria within a few minutes is the key to control infectious disease. However, rapid detection of pathogenic bacteria in clinical samples is quite a challenging task due to the complex matrix, as well as the low abundance of bacteria in real samples. Herein, we employ a label-free single-particle imaging approach to address this challenge. By tracking the scattering intensity variation of single particles in free solution, the morphological heterogeneity can be well identified with particle size smaller than the diffraction limit, facilitating the morphological identification of single bacteria from a complex matrix in a label-free manner. Furthermore, the manipulation of convection in free solution enables the rapid screening of low-abundance bacteria in a small field of view, which significantly improves the sensitivity of single-particle detection. As a proof of concept demonstration, we are able to differentiate the group B streptococci (GBS)–positive samples within 10 min from vaginal swabs without using any biological reagents. This is the most rapid and low-cost method to the best of our knowledge. We believe that such a single-particle imaging approach will find wider applications in clinical diagnosis and disease control due to its high sensitivity, rapidity, simplicity, and low cost.

In this review article, we review the 3 important aspects of linear-energy-transfer (LET) in intensity-modulated proton therapy (IMPT) for head and neck (H&N) cancer management. Accurate LET calculation methods are essential for LET-guided plan evaluation and optimization, which can be calculated either by analytical methods or by Monte Carlo (MC) simulations. Recently, some new 3D analytical approaches to calculate LET accurately and efficiently have been proposed. On the other hand, several fast MC codes have also been developed to speed up the MC simulation by simplifying nonessential physics models and/or using the graphics processor unit (GPU)–acceleration approach. Some concepts related to LET are also briefly summarized including (1) dose-weighted versus fluence-weighted LET; (2) restricted versus unrestricted LET; and (3) microdosimetry versus macrodosimetry. LET-guided plan evaluation has been clinically done in some proton centers. Recently, more and more studies using patient outcomes as the biological endpoint have shown a positive correlation between high LET and adverse events sites, indicating the importance of LET-guided plan evaluation in proton clinics. Various LET-guided plan optimization methods have been proposed to generate proton plans to achieve biologically optimized IMPT plans. Different optimization frameworks were used, including 2-step optimization, 1-step optimization, and worst-case robust optimization. They either indirectly or directly optimize the LET distribution in patients while trying to maintain the same dose distribution and plan robustness. It is important to consider the impact of uncertainties in LET-guided optimization (ie, LET-guided robust optimization) in IMPT, since IMPT is sensitive to uncertainties including both the dose and LET distributions. We believe that the advancement of the LET-guided plan evaluation and optimization will help us exploit the unique biological characteristics of proton beams to improve the therapeutic ratio of IMPT to treat H&N and other cancers.

BackgroundWe aimed to describe the chest CT findings in sixty-seven patients infected by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).MethodsWe retrospectively reviewed 67 patients hospitalized in Ruian People’s Hospital. All the patients received the positive diagnosis of SARS-CoV-2 infection. The CT and clinical data were collected between January 23rd, 2020 and February 10th, 2020. The CT images were analyzed by the senior radiologists.ResultsThere are 54 patients with positive CT findings and 13 patients with negative CT findings. The typical CT findings in hospitalized patients with SARS-CoV-2 infection were ground glass opacities (42/54), lesions located in the peripheral area (50/54), multiple lesions (46/54), and lesions located in the lower lobes (42/54). There were less typical CT findings, including air bronchogram (18/54), pleural thickening or pleural effusion (14/54), consolidation (12/54), lesions in the upper lobes (12/54), interlobular septal thickening (11/54), reversed halo sign (9/54), single lesion (8/54), air cavities (4/54), bronchial wall thickening (3/54), and intrathoracic lymph node enlargement (2/54).ConclusionsCT features can play an important role in the early diagnosis and follow-up of COVID-19 patients.

Purpose: There are very little data available comparing outcomes of intensity-modulated proton therapy (IMPT) to intensity-modulated radiation therapy (IMRT) in patients with locally advanced NSCLC (LA-NSCLC). Methods: Seventy-nine consecutively treated patients with LA-NSCLC underwent definitive IMPT (n = 33 [42%]) or IMRT (n = 46 [58%]) from 2016 to 2018 at our institution. Survival rates were calculated using the Kaplan-Meier method and compared with the log-rank test. Acute and subacute toxicities were graded based on Common Terminology Criteria for Adverse Events, version 4.03. Results: Median follow-up was 10.5 months (range, 1-27) for all surviving patients. Most were stage III (80%), received median radiation therapy (RT) dose of 60 Gy (range, 45-72), and had concurrent chemotherapy (65%). At baseline, the IMPT cohort was older (76 vs 69 years, P

Hydrocarbon gases, especially toxic ones like benzene and xylene, pose threats to human health and the environment. But existing detection techniques, like bulky GC-MS or portable PID, cannot fulfill people's requirement of affordable and reliable hydrocarbons monitoring for the purpose of personal exposure assessment. Here, a simple, low cost, and light hydrocarbon gases sensor using a smartphone camera as a readout was developed based on the paper based milli-cantilever bending induced by polymer swelling. Its sensing cantilever was composed of three layers: functional layer of polyethylene film, adhesive layer of double-side tape, and a substrate of weighing paper. And the dimensions of the milli-fabricated sensing cantilever are 8 mm long, 0.5 mm wide, and 50 μm thick. The sensor response was the displacement of milli-cantilever free end. As proof of concept, its performance to typical hydrocarbons of xylene, hexane, and BTEX was carefully examined. For all of them, the sensor showed good performance of linear response to hydrocarbon concentrations, wide detection range, low detection, and fast response. Taking xylene for example, the sensor showed wide detection range of 15-140 ppm, low detection limit of 15 ppm, and fast response of 30 s. The sensor cross-sensitivity to other hydrocarbons was consistent with polymer swelling theory that the more carbons the hydrocarbon has, the higher the sensor sensitivity. Taking advantage of the rough materials chosen and simple fabrication procedure, the developed sensors also had high stability with time, low cost, and good uniformity. The developed sensor is affordable both physically and financially, has good performance, could meet hydrocarbons monitoring requirements for occupational safety or air pollution in petroleum industry, and would benefit people's health.

The record of daily quality control (QC) items shows machine performance patterns and potentially provides warning messages for preventive actions. This study developed a neural network model that could predict the record and trend of data variations quantitively.The record of 24 QC items for a radiotherapy machine was investigated in our institute. The QC records were collected daily for 3 years. The stacked long short-term memory (LSTM) model was used to develop the neural network model. A total of 867 records were collected to predict the record for the next 5 days. To compare the stacked LSTM, the autoregressive integrated moving average model (ARIMA) was developed on the same data set. The accuracy of the model was quantified by the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (RThe mean MAE, RMSE, andIn this study, the stacked LSTM model can accurately predict the record and trend of QC items. Moreover, the stacked LSTM model is robust when applied to another radiotherapy machine. Predicting future performance record will foresee possible machine failure, allowing early machine maintenance and reducing unscheduled machine downtime.

Purpose: We aim to evaluate the robustness of multi-field IMRT and VMAT plans to target motion for left-sided BC radiotherapy. Methods: The 7-field hybrid IMRT (7F-H-IMRT) and 2-arc VMAT (2A-VMAT) plans were generated for ten left-sided BC patients. Shifts of 3 mm, 5 mm, and 10 mm in six directions were introduced and the perturbed dose distributions were recalculated. The dose differences (∆D) of the original plan and perturbed plan corresponded to the plan robustness for the structure. Results: Higher ∆D98%, ∆D95%, and ∆Dmean of CTV were observed in 2A-VMAT plans, which induced higher tumor control probability reductions. A higher ∆Dmean of CTV Boost was found in 7F-H-IMRT plans despite lower ∆D98% and ∆D95%. Shifts in the S-I direction exerted the largest effect on CTV and CTV Boost. Regarding OARs, shifts in R, P, and I directions contributed to increasing the received dose. The 2A-VMAT plans performed better dose sparing, but had a higher robustness in a high-dose volume of the left lung and heart. The 2A-VMAT plans decreased the max dose of LAD but exhibited lower robustness. Conclusion: The 2A-VMAT plans showed higher sensitivity to position deviation. Shifts in the S-I direction exerted the largest effect for CTV and CTV Boost.

Purpose Approximate dose calculation methods were used in the nominal dose distribution and the perturbed dose distributions due to uncertainties in a commercial treatment planning system (CTPS) for robust optimization in intensity-modulated proton therapy (IMPT). We aimed to investigate whether the approximations influence plan quality, robustness, and interplay effect of the resulting IMPT plans for the treatment of locally advanced lung cancer patients. Materials and methods Ten consecutively treated locally advanced nonsmall cell lung cancer (NSCLC) patients were selected. Two IMPT plans were created for each patient using our in-house developed TPS, named "Solo," and also the CTPS, EclipseTM (Varian Medical Systems, Palo Alto, CA, USA), respectively. The plans were designed to deliver prescription doses to internal target volumes (ITV) drawn by a physician on averaged four-dimensional computed tomography (4D-CT). Solo plans were imported back to CTPS, and recalculated in CTPS for fair comparison. Both plans were further verified for each patient by recalculating doses in the inhalation and exhalation phases to ensure that all plans met clinical requirements. Plan robustness was quantified on all phases using dose-volume-histograms (DVH) indices in the worst-case scenario. The interplay effect was evaluated for every plan using an in-house developed software, which randomized starting phases of each field per fraction and accumulated dose in the exhalation phase based on the patient's breathing motion pattern and the proton spot delivery in a time-dependent fashion. DVH indices were compared using Wilcoxon rank-sum test. Results Compared to the plans generated using CTPS on the averaged CT, Solo plans had significantly better target dose coverage and homogeneity (normalized by the prescription dose) in the worst-case scenario [ITV D95% : 98.04% vs 96.28%, Solo vs CTPS, P = 0.020; ITV D5% -D95% : 7.20% vs 9.03%, P = 0.049] while all DVH indices were comparable in the nominal scenario. On the inhalation phase, Solo plans had better target dose coverage and cord Dmax in the nominal scenario [ITV D95% : 99.36% vs 98.45%, Solo vs CTPS, P = 0.014; cord Dmax : 20.07 vs 23.71 Gy(RBE), P = 0.027] with better target coverage and cord Dmax in the worst-case scenario [ITV D95% : 97.89% vs 96.47%, Solo vs CTPS, P = 0.037; cord Dmax : 24.57 vs 28.14 Gy(RBE), P = 0.037]. On the exhalation phase, similar phenomena were observed in the nominal scenario [ITV D95% : 99.63% vs 98.87%, Solo vs CTPS, P = 0.037; cord Dmax : 19.67 vs 23.66 Gy(RBE), P = 0.039] and in the worst-case scenario [ITV D95% : 98.20% vs 96.74%, Solo vs CTPS, P = 0.027; cord Dmax : 23.47 vs 27.93 Gy(RBE), P = 0.027]. In terms of interplay effect, plans generated by Solo had significantly better target dose coverage and homogeneity, less hot spots, and lower esophageal Dmean , and cord Dmax [ITV D95% : 101.81% vs 98.68%, Solo vs CTPS, P = 0.002; ITV D5% -D95% : 2.94% vs 7.51%, P = 0.002; cord Dmax : 18.87 vs 22.29 Gy(RBE), P = 0.014]. Conclusions Solo-generated IMPT plans provide improved cord sparing, better target robustness in all considered phases, and reduced interplay effect compared with CTPS. Consequently, the approximation methods currently used in commercial TPS programs may have space for improvement in generating optimal IMPT plans for patient cases with locally advanced lung cancer.

Background Esophageal carcinoma is the eighth most common cancer in the world. Volumetric-modulated arc therapy (VMAT) is widely used to treat distal esophageal carcinoma due to high conformality to the target and good sparing of organs at risk (OAR). It is not clear if small-spot intensity-modulated proton therapy (IMPT) demonstrates a dosimetric advantage over VMAT. In this study, we compared dosimetric performance of VMAT and small-spot IMPT for distal esophageal carcinoma in terms of plan quality, plan robustness, and interplay effects. Methods 35 distal esophageal carcinoma patients were retrospectively reviewed; 19 patients received small-spot IMPT and the remaining 16 of them received VMAT. Both plans were generated by delivering prescription doses to clinical target volumes (CTVs) on phase-averaged 4D-CT's. The dose-volume-histogram (DVH) band method was used to quantify plan robustness. Software was developed to evaluate interplay effects with randomized starting phases for each field per fraction. DVH indices were compared using Wilcoxon rank-sum test. For fair comparison, all the treatment plans were normalized to have the same CTVhigh D95% in the nominal scenario relative to the prescription dose. Results In the nominal scenario, small-spot IMPT delivered statistically significantly lower liver Dmean and V30Gy[RBE] , lung Dmean , heart Dmean compared with VMAT. CTVhigh dose homogeneity and protection of other OARs were comparable between the two treatments. In terms of plan robustness, the IMPT and VMAT plans were comparable for kidney V18Gy[RBE] , liver V30Gy[RBE] , stomach V45Gy[RBE] , lung Dmean , V5Gy[RBE] , and V20Gy[RBE] , cord Dmax and D 0.03 c m 3 , liver Dmean , heart V20Gy[RBE] , and V30Gy[RBE] , but IMPT was significantly worse for CTVhigh D95% , D 2 c m 3 , and D5% -D95% , CTVlow D95% , heart Dmean , and V40Gy[RBE] , requiring careful and experienced adjustments during the planning process and robustness considerations. The small-spot IMPT plans still met the standard clinical requirements after interplay effects were considered. Conclusions Small-spot IMPT decreases doses to heart, liver, and total lung compared to VMAT as well as achieves clinically acceptable plan robustness. Our study supports the use of small-spot IMPT for the treatment of distal esophageal carcinoma.

Gastric cancer (GC), a leading cause of cancer-related death, is a heterogeneous disease. We aim to describe clinically relevant molecular classifications of GC that incorporate heterogeneity and provide useful clinical information. We combined different gene expression datasets and filtered a 7-gene signature related to the extracellular matrix (ECM), which also exhibited significant prognostic value in GC patients. Interestingly, putative CCCTC-binding factor (CTCF) regulatory elements were identified within the promoters of these ECM-related genes and were confirmed by chromatin immunoprecipitation sequencing (ChIP-Seq). CTCF binding sites also overlapped with histone activation markers, indicating direct regulation. In addition, CTCF was also correlated with the Wnt signaling pathway. A comparison of human GC cell lines with high or low expression of ECM-related genes revealed different levels of tumor aggressiveness, suggesting the cancer development-promoting functions of ECM-related genes. Furthermore, CTCF regulated COL1A1 and COLA31 expression in vitro. Silencing CTCF or COL1A1/COL1A3 markedly inhibited cell growth and migration in the metastatic GC cell line BGC823. Collectively, this ECM-related 7-gene signature provides a novel insight for survival prediction among GC patients. The zinc finger protein CTCF regulates ECM-related genes, thereby promoting GC cell growth and migration.

Using a camera as an optical sensor to monitor physiological parameters has garnered considerable research interest in biomedical engineering in recent decades. Researchers have explored the use of a camera for monitoring a variety of physiological waveforms, together with the vital signs carried by these waveforms. Most of the obtained waveforms are related to the human respiratory and cardiovascular systems, and in addition of being indicative of overall health, they can also detect early signs of certain diseases. While using a camera for noncontact physiological signal monitoring offers the advantages of low cost and operational ease, it also has the disadvantages such as vulnerability to motion and lack of burden-free calibration solutions in some use cases. This study presents an overview of the existing camera-based methods that have been reported in recent years. It introduces the physiological principles behind these methods, signal acquisition approaches, various types of acquired signals, data processing algorithms, and application scenarios of these methods. It also discusses the technological gaps between the camera-based methods and traditional medical techniques, which are mostly contact-based. Furthermore, we present the manner in which noncontact physiological signal monitoring use has been extended, particularly over the recent years, to more day-to-day aspects of individuals' lives, so as to go beyond the more conventional use case scenarios. We also report on the development of novel approaches that facilitate easier measurement of less often monitored and recorded physiological signals. These have the potential of ushering a host of new medical and lifestyle applications. We hope this study can provide useful information to the researchers in the noncontact physiological signal measurement community.

Hepatocellular carcinoma is one of the most common malignant tumors worldwide and the fourth leading cause of cancer-related deaths. The prognosis of hepatocellular carcinoma patients is extremely poor due to the occult onset and high metastasis of hepatocellular carcinoma. Therefore, biomarkers with high specificity and sensitivity are of great importance in early screening, diagnosis prognosis, and treatment of hepatocellular carcinoma patients. Exosomes are tiny vesicles secreted by various types of cells, which can serve as mediators of intercellular communication to regulate the tumor microenvironment, and play a key role in the occurrence, development, prognosis, monitor and treatment of hepatocellular carcinoma. As microRNA deliverer, exosomes are involved in multiple life activities by regulating target genes of recipient cells such as proliferation, invasion, metastasis and apoptosis of cancer cells. In this review, we summarized the composition, active mechanism and function of exosomal microRNAs in hepatocellular carcinoma, and elaborated on their potential application value of early diagnosis and treatment in hepatocellular carcinoma.

MicroRNAs (miRNAs or miRs) are non-coding small RNAs that target specific messenger RNAs to inhibit protein translation. miR-200a and miR-141 function as tumor suppressors by targeting STAT4. These two miRNAs belong to the same family, and their expression is often decreased in various cancer types, but are located on different chromosomes of the human genome. The present study showed that the expression levels of miR-141 and miR-200a in serum and cells of liver cancer are significantly downregulated. The expression levels of miR-141 and miR-200a are closely associated with clinicopathological features of liver cancer, especially metastasis and invasion. It is first reported that STAT4 is the new common target gene of miR-141 and miR-200a. In the present study, miR-141 and miR-200a were confirmed to inhibit the expression of E-cadherin and vimentin synergistically during epithelial-mesenchymal transition to regulate the proliferation, migration and invasion of liver cancer cells by targeting STAT4. Simultaneous overexpression of miR-200a and miR-141 resulted in stronger effects compared with each miRNA alone. In addition, overexpression of STAT4 significantly reversed the tumor suppressive roles of miR-200a and miR-141 in liver cancer cells. These findings enrich the tumor suppressor mechanisms of the miR-200 family, and may also provide new experimental and theoretical basis for the use of miRNAs for early diagnosis, prognosis and thorough treatment of liver cancer.

The increase of proton beam number might provide higher degrees of freedom in the optimization of intensity-modulated proton therapy planning. In this study, we aimed to quantitatively explore the potential benefits of the increased beam number, including dose volume histogram (DVH), linear energy transfer volume histogram, and DVH bandwidth metrics. Twelve patients with lung cancer are retrospectively selected. Four plans were created based on internal target volume (ITV) robust optimization for each patient using the RayStation treatment planning system. Four plans were generated using different numbers (three, five, seven, and nine) of evenly separated coplanar beams. The three-beam plan was considered as the reference plan. Biologically equivalent doses were calculated using both constant relative biological effectiveness (RBE) and variable RBE models, respectively. To evaluate plan quality, DVH metrics in the target [ITV: D2%, CI, HI] and organs-at-risk [Lung: V5Gy[RBE], V20Gy[RBE], V30Gy[RBE]; Heart D2%; Spinal cord D2%] were calculated using both RBE models. To evaluate LET distributions, LET volume histogram metrics [ITV LETmean and LET2%; Lung LETmean and LET2%; Heart LET2%; Spinal cord LET2%] were quantified. To evaluate plan robustness, the metrics using DVH bandwidth [ITV: D2%, D99%; Lung: V5Gy[RBE], V20Gy[RBE], V30Gy[RBE]; Heart D2%; Spinal cord D2%] were also reported. For plan quality, the increase of proton beam number resulted in fewer target hot spots, improved target dose conformity, improved target dose homogeneity, lower median-dose lung volume, and fewer hot spots in spinal cord. As to LET distributions, target mean LET increased significantly as the beam number increased to seven or more. Lung LET hot spots were significantly reduced with the increase of proton beams. With respect to plan robustness, the robustness of target dose coverage, target hot spots, and low-dose lung volume were improved, while the robustness of heart hot spots became worse as the beam number increased to nine. The robustness of cord hot spots became worse using five and seven beams compared to that using three beams. As the proton beam number increased, plan quality and LET distributions were comparable or significantly improved. The robustness of target dose coverage, target dose hot spots, and low-dose lung volume were significantly improved.

Hepatocellular carcinoma (HCC) is one of the most common clinically malignant tumors of the digestive system. It ranks the sixth most common malignant tumor in the world and ranks fourth among cancer-related death worldwide. At present, early diagnosis and prognosis monitoring of hepatocellular carcinoma mainly use alpha-fetoprotein combined with ultrasonography, which leads to clinical frequently missed diagnosis or even misdiagnosis. Therefore, seeking specific diagnostic and monitoring molecules of hepatocellular carcinoma are still hot topics in contemporary medical practice. MicroRNA is an endogenous non-coding small RNA that regulates the expression of the target molecule and participates in various biological processes in vivo. The miR-200 family, the most common celebrity family of microRNAs, is commonly lower expression in a variety of cancers and is closely associated with tumorigenesis and outcome, especially hepatocellular carcinoma. This review mainly discusses the expression changes, specific molecular mechanisms, biological functions and clinical values of miR-200 family in hepatocellular carcinoma. Moreover, we highlighted utilization of miR-200 family as molecular biomarkers for early diagnosis, prognostic monitoring and appropriate therapy in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common malignant tumor in the digestive tract with limited therapeutic choices. Intercellular communication among cancer cells and their microenvironment is crucial to disease progression. Exosomes are extracellular vesicles secreted by multiple types of cells into the extracellular space, which contain a variety of active components of secretory cells, including lipids, proteins, RNA and DNA. This vesicle structure involves in the exchange of materials and information between cells and plays an important role in the development of many diseases. Studies have shown that exosomes participate in the communication between HCC cells and non-HCC cells and regulate the occurrence and development of hepatocellular carcinoma. Therefore, exosomes may be specific biomarkers for early diagnosis and metastasis of HCC, which are also potential targets for the treatment of HCC. This review summarizes the characteristic, types and biological functions of exosomes and discusses their research progress and application prospects in the diagnosis and treatment of HCC.

Rationale: Many clinical studies have focused on the epidemiological and clinical characteristics of inpatients with coronavirus disease (COVID-19). However, there are few reports about the clinical follow-up of discharged patients. Objectives: To describe the follow-up of patients with COVID-19 in Wenzhou City, Zhejiang, China. Methods: We retrospectively reviewed 4-week follow-ups in patients with COVID-19, including computed tomographic (CT) chest scanning, blood testing, and oropharyngeal-swab testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ribonucleic acid. The chest CT scans and blood tests were performed on the last day before discharge and 2 weeks and 4 weeks after discharge. The oropharyngeal-swab tests were performed at both 1 week and 2 weeks after discharge. Fifty-one patients with common COVID-19 were enrolled in the study. All the CT and clinical data were collected between January 23 and March 28, 2020. Results: Compared with the abnormalities found on the the last CT scans before discharge, the abnormalities in the lungs at the first and second follow-ups after discharge had been gradually absorbed. The cases with focal ground-glass opacity were reduced from 17.7% to 9.8% of cases. The cases with multiple ground-glass opacities decreased from 80.4% to 23.5%. The cases with consolidation were reduced from 49.0% to 2.0%. The cases with interlobular septal thickening were reduced from 80.4% to 35.3%. The cases with subpleural lines were reduced from 29.4% to 7.8%. The cases with irregular lines were reduced from 41.2% to 15.7%. The lung lesions of 25.5% patients were shown to be fully absorbed on the first CT scans after discharge, and the rate of lung recovery increased to 64.7% after the second follow-up. Nucleic-acid test results became recurrently positive in 17.6% of discharged patients, of whom only 33.3% complained of clinical symptoms. There were no differences in the characteristics of the last CT scans before discharge between the patients with recurrently positive test results and the patients with negative test results. The lung damage was fully absorbed in 55.6% of discharged patients with recurrence of positive test results for SARS-CoV-2 ribonucleic acid. Conclusions: The lung damage due to COVID-19 could be reversible for patients with common COVID-19. A few cases showed recurring positive results of nucleic-acid tests after discharge.

Background Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground-glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray-level co-occurrence matrix (GLCM) features, 15 gray-level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High-dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each feature was the average weights calculated by ReliefF in n times. Features with relevance larger than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using tenfold cross-validation. Results and conclusions The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

Intensity-modulated proton therapy (IMPT) is known to be sensitive to patient setup and range uncertainty issues. Multiple robust optimization methods have been developed to mitigate the impact of these uncertainties. Here, we propose a new robust optimization method, which provides an alternative way of robust optimization in IMPT, and is clinically practical, which will enable users to control the balance between nominal plan quality and plan robustness in a user-defined fashion.We calculated nine individual dose distributions which corresponded to one nominal and eight extreme scenarios caused by patient setup and proton beam's range uncertainties. For each voxel, the normalized dose interval (NDI) is defined as the full dose range variation divided by the maximum dose in all uncertainty scenarios (NDI = [max - min dose]/max dose), which was then used to calculate the normalized dose interval volume histogram (NDIVH) curves. The areas under the NDIVH curves were used to quantify plan robustness. A normalized dose interval volume constraint (NDIVC) applied to the target was incorporated to specify the desired robustness which was user-defined. Users could then explore the trade-off between nominal plan quality and plan robustness by adjusting the position of the NDIVCs on the NDIVH curves freely. We benchmarked our method using one lung, five head and neck (HN), and three prostate cases by comparing our results to those derived using the voxel-wise worst-case robust optimization.Using the benchmark cases, our new method achieved quality IMPT plans comparable to those derived from the voxel-wise worst-case robust optimization for both nominal plan quality and plan robustness in general; even more conformal and more homogeneous target dose distributions in some cases, if proper NDIVCs were applied. The AUC under NDIVH, as a precise quantitative index of plan robustness, was consistent with DVH bandwidths. Additionally, we demonstrated the feasibility of adjusting the position of NDIVCs in the NDIVH curves which allowed users to explore the trade-off between nominal plan quality and plan robustness.The NDIVH-based robust optimization method provided a novel and individualized way of robust optimization in IMPT, and enables users to adjust the balance between nominal plan quality and plan robustness in a user-defined fashion. This method is applicable for continued improvement and developing the next generation of IMPT planning algorithms in the future.