Coded aperture compressive temporal imaging via unsupervised lightweight local-global networks with geometric characteristics.

Coded aperture compressive temporal imaging (CACTI) utilizes compressive sensing (CS) theory to compress three dimensional (3D) signals into 2D measurements for sampling in a single snapshot measurement, which in turn acquires high-dimensional (HD) visual signals. To solve the problems of low quality and slow runtime often encountered in reconstruction, deep learning has become the mainstream for signal reconstruction and has shown superior performance. Currently, however, impressive networks are typically supervised networks with large-sized models and require vast training sets that can be difficult to obtain or expensive. This limits their application in real optical imaging systems. In this paper, we propose a lightweight reconstruction network that recovers HD signals only from compressed measurements with noise and design a block consisting of convolution to extract and fuse local and global features, stacking multiple features to form a lightweight architecture. In addition, we also obtain unsupervised loss functions based on the geometric characteristics of the signal to guarantee the powerful generalization capability of the network in order to approximate the reconstruction process of real optical systems. Experimental results show that our proposed network significantly reduces the model size and not only has high performance in recovering dynamic scenes, but the unsupervised video reconstruction network can approximate its supervised version in terms of reconstruction performance.

Effect of Ephestia kuehniella Eggs on Development and Transcriptome of the Ladybird Beetle Propylea japonica.

The eggs of the Mediterranean flour moth, Ephestia kuehniella, are frequently utilized as alternative diets and have demonstrated promising outcomes when consumed by various insects. Nonetheless, the specific reasons for their effectiveness remain unclear. In our study, we assessed the developmental performance of the ladybird Propylea japonica when fed E. kuehniella eggs, alongside 12 factitious prey or artificial diets. Our findings revealed that ladybirds fed E. kuehniella eggs displayed a performance comparable to those fed the natural prey Megoura crassicauda. Transcriptome profiling of larvae raised on E. kuehniella eggs and M. crassicauda revealed that genes upregulated in the former group were enriched in metabolic pathways associated with carbohydrates, lipids, and other essential nutrients. This suggests that E. kuehniella eggs may have a higher nutrient content compared to natural prey. Furthermore, a notable downregulation in the expression of immune effector genes, such as Attacin and Coleoptericin, was observed, which might be attributed to the lower microbial content in E. kuehniella eggs compared to M. crassicauda. We suggest that the difference between E. kuehniella eggs and M. crassicauda as food sources for P. japonica lies in their nutrient and microbial contents. These findings provide valuable insights for the advancement of innovative artificial breeding systems for natural enemies.

Conversion-type anode chemistry with interfacial compatibility toward Ah-level near-neutral high-voltage zinc ion batteries.

High-voltage aqueous zinc ion batteries (AZIBs) with a high-safety near-neutral electrolyte is of great significance for practical sustainable application; however, they suffer from anode and electrode/electrolyte interfacial incompatibility. Herein, a conversion-type anode chemistry with a low anodic potential, which is guided by the Gibbs free energy change of conversion reaction, was designed for high-voltage near-neutral AZIBs. A reversible conversion reaction between ZnC2O4·2H2O particles and three-dimensional Zn metal networks well-matched in CH3COOLi-based electrolyte was revealed. This mechanism can be universally validated in the battery systems with sodium or iodine ions. More importantly, a cathodic crowded micellar electrolyte with a water confinement effect was proposed in which lies the core for the stability and reversibility of the cathode under an operating platform voltage beyond 2.0 V, obtaining a capacity retention of 95% after 100 cycles. Remarkably, the scientific and technological challenges from the coin cell to Ah-scale battery, sluggish kinetics of the solid-solid electrode reaction, capacity excitation under high loading of active material, and preparation complexities associated with large-area quasi-solid electrolytes, were explored, successfully achieving an 88% capacity retention under high loading of more than 20 mg cm-2 and particularly a practical 1.1 Ah-level pouch cell. This work provides a path for designing low-cost, eco-friendly and high-voltage aqueous batteries.

Development of an indirect competitive ELISA based on the common epitope of fish parvalbumin for its detection.

A common epitope (AGSFDHKKFFKACGLSGKST) of parvalbumin from 16 fish species was excavated using bioinformatics tools combined with the characterization of fish parvalbumin binding profile of anti-single epitope antibody in this study. A competitive enzyme-linked immunosorbent assay (ELISA) based on the common epitope was established with a limit of detection of 10.15 ng/mL and a limit of quantification of 49.29 ng/mL. The developed ELISA exhibited a narrow range (71% to 107%) of related cross-reactivity of 15 fish parvalbumin. Besides, the recovery, the coefficient of variations for the intra-assay and the inter-assay were 84.3% to 108.2%, 7.4% to 13.9% and 8.5% to 15.6%. Our findings provide a novel idea for the development of a broad detection method for fish allergens and a practical tool for the detection of parvalbumin of economic fish species in food samples.

Rates and Predictors of Pain Reduction With Intracranial Stimulation for Intractable Pain Disorders.

BACKGROUND AND OBJECTIVES: Intracranial modulation paradigms, namely deep brain stimulation (DBS) and motor cortex stimulation (MCS), have been used to treat intractable pain disorders. However, treatment efficacy remains heterogeneous, and factors associated with pain reduction are not completely understood. METHODS: We performed an individual patient review of pain outcomes (visual analog scale, quality-of-life measures, complications, pulse generator implant rate, cessation of stimulation) after implantation of DBS or MCS devices. We evaluated 663 patients from 36 study groups and stratified outcomes by pain etiology and implantation targets. RESULTS: Included studies comprised primarily retrospective cohort studies. MCS patients had a similar externalized trial success rate compared with DBS patients (86% vs 81%; P = .16), whereas patients with peripheral pain had a higher trial success rate compared with patients with central pain (88% vs 79%; P = .004). Complication rates were similar for MCS and DBS patients (12% vs 15%; P = .79). Patients with peripheral pain had lower likelihood of device cessation compared with those with central pain (5.7% vs 10%; P = .03). Of all implanted patients, mean pain reduction at last follow-up was 45.8% (95% CI: 40.3-51.2) with a 31.2% (95% CI: 12.4-50.1) improvement in quality of life. No difference was seen between MCS patients (43.8%; 95% CI: 36.7-58.2) and DBS patients (48.6%; 95% CI: 39.2-58) or central (41.5%; 95% CI: 34.8-48.2) and peripheral (46.7%; 95% CI: 38.9-54.5) etiologies. Multivariate analysis identified the anterior cingulate cortex target to be associated with worse pain reduction, while postherpetic neuralgia was a positive prognostic factor. CONCLUSION: Both DBS and MCS have similar efficacy and complication rates in the treatment of intractable pain. Patients with central pain disorders tended to have lower trial success and higher rates of device cessation. Additional prognostic factors include anterior cingulate cortex targeting and postherpetic neuralgia diagnosis. These findings underscore intracranial neurostimulation as an important modality for treatment of intractable pain disorders.

Controllable Pseudospin Topological Add-Drop Filter Based on Magnetic-Optical Photonic Crystals.

We propose a controllable topological add-drop filter based on magnetic-optical photonic crystals. This add-drop filter is composed of two straight waveguides and a hexagonal photonic crystal ring resonator. The waveguide and ring resonator are constructed by three different honeycomb magnetic-optical photonic crystals. The expanded lattice is applied with an external magnetic field so that it breaks time-reversal symmetry and the analogous quantum spin Hall effect simultaneously. While the standard one and the compressed one are not magnetized and trivial, the straight waveguide supports pseudospin-down (or pseudospin-up) one-way states when the expanded lattice is applied with an external magnetic field of +H (or -H). The ring resonator possesses multiple resonant modes which can be divided into travelling modes and standing modes. By using the travelling modes, we have demonstrated the function of the add-drop filter and realized the output port control by changing the direction of the magnetic field. Moreover, a large tunable power ratio from near 0 to 52.6 is achieved by adjusting the strength of the external magnetic field. The structure has strong robustness against defects due to the topological protection property. These results have potential in wavelength division multiplexing systems and integrated topological optical devices.

Non-invasive detection of systemic lupus erythematosus using SERS serum detection technology and deep learning algorithms.

Systemic lupus erythematosus (SLE) is an autoimmune disease with multiple symptoms, and its rapid screening is the research focus of surface-enhanced Raman scattering (SERS) technology. In this study, gold@silver-porous silicon (Au@Ag-PSi) composite substrates were synthesized by electrochemical etching and in-situ reduction methods, which showed excellent sensitivity and accuracy in the detection of rhodamine 6G (R6G) and serum from SLE patients. SERS technology was combined with deep learning algorithms to model serum features using selected CNN, AlexNet, and RF models. 92 % accuracy was achieved in classifying SLE patients by CNN models, and the reliability of these models in accurately identifying sera was verified by ROC curve analysis. This study highlights the great potential of Au@Ag-PSi substrate in SERS detection and introduces a novel deep learning approach for SERS for accurate screening of SLE. The proposed method and composite substrate provide significant value for rapid, accurate, and noninvasive SLE screening and provide insights into SERS-based diagnostic techniques.

Crucial Roles of Ethyl Methyl Carbonate in Lithium-Ion and Dual-Ion Batteries: A Review.

The essential role of electrolyte solutions in traditional electrochemical energy storage devices is crucial to enhancing their performance. Consequently, a wide array of electrolyte mixtures along with diverse electrodes have been extensively explored across different models of secondary batteries. Fascinatingly, the role of ethyl methyl carbonate (EMC) as a key cosolvent in the electrolyte mixture of commercial lithium-ion batteries with a graphite anode is garnering growing attention in alternative rechargeable dual-ion batteries utilizing graphite cathodes. In this context, the advancement and function of EMC as a solvent in electrolyte mixtures for lithium-ion and dual-ion batteries were extensively and thoroughly examined in this analysis, encompassing the genesis, synthesis process, and diverse characteristics for the practical uses of these batteries. Here, the review aims to guide readers in understanding EMC's function and impact as a cosolvent in electrolyte mixtures for both major secondary lithium-ion and dual-ion batteries, considering their distinct physicochemical characteristics.

Naturalistic acute pain states decoded from neural and facial dynamics.

Pain is a complex experience that remains largely unexplored in naturalistic contexts, hindering our understanding of its neurobehavioral representation in ecologically valid settings. To address this, we employed a multimodal, data-driven approach integrating intracranial electroencephalography, pain self-reports, and facial expression quantification to characterize the neural and behavioral correlates of naturalistic acute pain in twelve epilepsy patients undergoing continuous monitoring with neural and audiovisual recordings. High self-reported pain states were associated with elevated blood pressure, increased pain medication use, and distinct facial muscle activations. Using machine learning, we successfully decoded individual participants' high versus low self-reported pain states from distributed neural activity patterns (mean AUC = 0.70), involving mesolimbic regions, striatum, and temporoparietal cortex. High self-reported pain states exhibited increased low-frequency activity in temporoparietal areas and decreased high-frequency activity in mesolimbic regions (hippocampus, cingulate, and orbitofrontal cortex) compared to low pain states. This neural pain representation remained stable for hours and was modulated by pain onset and relief. Objective facial expression changes also classified self-reported pain states, with results concordant with electrophysiological predictions. Importantly, we identified transient periods of momentary pain as a distinct naturalistic acute pain measure, which could be reliably differentiated from affect-neutral periods using intracranial and facial features, albeit with neural and facial patterns distinct from self-reported pain. These findings reveal reliable neurobehavioral markers of naturalistic acute pain across contexts and timescales, underscoring the potential for developing personalized pain interventions in real-world settings.

Design and Algorithm Integration of High-Precision Adaptive Underwater Detection System Based on MEMS Vector Hydrophone.

Real-time DOA (direction of arrival) estimation of surface or underwater targets is of great significance to the research of marine environment and national security protection. When conducting real-time DOA estimation of underwater targets, it can be difficult to extract the prior characteristics of noise due to the complexity and variability of the marine environment. Therefore, the accuracy of target orientation in the absence of a known noise is significantly reduced, thereby presenting an additional challenge for the DOA estimation of the marine targets in real-time. Aiming at the problem of real-time DOA estimation of acoustic targets in complex environments, this paper applies the MEMS vector hydrophone with a small size and high sensitivity to sense the conditions of the ocean environment and change the structural parameters in the adaptive adjustments system itself to obtain the desired target signal, proposes a signal processing method when the prior characteristics of noise are unknown. Theoretical analysis and experimental verification show that the method can achieve accurate real-time DOA estimation of the target, achieve an error within 3.1° under the SNR (signal-to-noise ratio) of the X channel of -17 dB, and maintain a stable value when the SNR continues to decrease. The results show that this method has a very broad application prospect in the field of ocean monitoring.

SLE diagnosis research based on SERS combined with a multi-modal fusion method.

As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future.

Extensible multi-wavelength interrogation method for arbitrary cavities in low-fineness multi-cavity Fabry-Pérot interferometric sensors.

To the best of our knowledge, a novel extensible multi-wavelength (EMW) method to interrogate arbitrary cavities in low-fineness fiber-optic multi-cavity Fabry-Pérot interferometric (LFMFPI) sensors is proposed and experimentally demonstrated. Based on the derived model of the LFMFPI sensor with any amount of cascaded cavities, theoretically, variation in each cavity of a LFMFPI sensor can be extracted simultaneously once the necessary parameters are acquired in advance. The feasibility of this method is successfully demonstrated in simulations and experiments utilizing LFMFPI sensors. In experiments with the LFMFPI sensor, optical path differences (OPD) of 78 nm and 2.95 µm introduced by temperature variation in two cavities, and the OPD induced by vibration with the amplitude from 5.891 nm to 38.116 nm were extracted, respectively. The EMW method is potential in multi-parameter sensing for pressure, vibration, and temperature.

Metabolism, digestion, and horizontal transfer: potential roles and interaction of symbiotic bacteria in the ladybird beetle Novius pumilus and their prey Icerya aegyptiaca.

In this study, we first time sequenced and analyzed the 16S rRNA gene data of predator ladybird beetles Novius pumilus and globally distributed invasive pest Icerya aegyptiaca at different stages, and combined data with bacterial genome sequences in N. pumilus to explored the taxonomic distribution, alpha and beta diversity, differentially abundant bacteria, co-occurrence network, and putative functions of their microbial community. Our finding revealed that Candidatus Walczuchella, which exhibited a higher abundance in I. aegyptiaca, possessed several genes in essential amino acid biosynthesis and seemed to perform roles in providing nutrients to the host, similar to other obligate symbionts in scale insects. Lactococcus, Serratia, and Pseudomonas, more abundant in N. pumilus, were predicted to have genes related to hydrocarbon, fatty acids, and chitin degradation, which may assist their hosts in digesting the wax shell covering the scale insects. Notably, our result showed that Lactococcus had relatively higher abundances in adults and eggs compared to other stages in N. pumilus, indicating potential vertical transmission. Additionally, we found that Arsenophonus, known to influence sex ratios in whitefly and wasp, may also function in I. aegyptiaca, probably by influencing nutrient metabolism as it similarly had many genes corresponding to vitamin B and essential amino acid biosynthesis. Also, we observed a potential horizontal transfer of Arsenophonus between the scale insect and its predator, with a relatively high abundance in the ladybirds compared to other bacteria from the scale insects.IMPORTANCEThe composition and dynamic changes of microbiome in different developmental stages of ladybird beetles Novius pumilus with its prey Icerya aegyptiaca were detected. We found that Candidatus Walczuchella, abundant in I. aegyptiaca, probably provide nutrients to their host based on their amino acid biosynthesis-related genes. Abundant symbionts in N. pumilus, including Lactococcus, Serratia, and Pseudophonus, may help the host digest the scale insects with their hydrocarbon, fatty acid, and chitin degrading-related genes. A key endosymbiont Arsenophonus may play potential roles in the nutrient metabolisms and sex determination in I. aegyptiaca, and is possibly transferred from the scale insect to the predator.

iSPAN: Explainable prediction of outcomes post thrombectomy with Machine Learning.

PURPOSE: This study aimed to develop and evaluate a machine learning model and a novel clinical score for predicting outcomes in stroke patients undergoing endovascular thrombectomy. MATERIALS AND METHODS: This retrospective study included all patients aged over 18 years with an anterior circulation stroke treated at a thrombectomy centre from 2010 to 2020 with external validation. The primary outcome was day 90 mRS ≥3. Existing clinical scores (SPAN and PRE) and Machine Learning (ML) models were compared. A novel clinical score (iSPAN) was derived by adding an optimised weighting of the most important ML features to the SPAN. RESULTS: 812 patients were initially included (397 female, average age 73), 63 for external validation. The best performing clinical score and ML model were SPAN and XGB (sensitivity, specificity and accuracy 0.290, 0.967, 0.628 and 0.693, 0.783, 0.738 respectively). A significant difference was found overall and our XGB model was more accurate than SPAN (p < 0.0018). The most important features were Age, mTICI and total number of passes. The addition of 11 points for mTICI of ≤2B and 3 points for ≥3 passes to the SPAN achieved the best accuracy and was used to create the iSPAN. iSPAN was not significantly less accurate than our XGB model (p > 0.5). In the external validation set, iSPAN and SPAN achieved sensitivity, specificity, and accuracy of (0.735, 0.862, 0.79) and (0.471, 0.897, 0.67) respectively. CONCLUSION: iSPAN incorporates machine-derived features to achieve better predictions compared to existing clinical scores. It is not inferior to our XGB model and is externally generalisable.

Conversion-intercalation competing behaviour of halogen storage on graphite electrode from fluid ZnCl2/ZnBr2 hydrates.

Zinc-based aqueous dual-ion batteries (ADIBs) with halogen-graphite intercalation compound positive electrodes are among the most competitive candidates for next-generation electrochemical energy storage systems. However, most of the electrolytes employed have been gel-like electrolytes; hence, a fundamental understanding of the halogen storage process using fluid hydrates will be essential for constructing efficient Zn-based ADIBs. Herein, the halogen storage mechanism on a graphite electrode from fluid ZnCl2/ZnBr2 hydrates is studied by experimental and computational methods. The results indicate that the halogen storage mechanism is a competition between conversion and intercalation. Moreover, the macroscopic electrode reaction is determined by both the ion-pair solvation state at the graphite-electrolyte interface and the subsequent reactant supply is influenced by the electrode reaction rate.

EXOSC5 maintains cancer stem cell activity in endometrial cancer by regulating the NTN4/integrin ß1 signalling axis.

Endometrial carcinoma (EC) is a common type of uterine cancer in developed countries, originating from the uterine epithelium. The incidence rate of EC in Taiwan has doubled from 2005. Cancer stem cells (CSCs) are a subpopulation of cancer cells that have high tumorigenicity and play a crucial role in the malignant processes of cancer. Targeting molecules associated with CSCs is essential for effective cancer treatments. This study delves into the role of Exosome component 5 (EXOSC5) in EC. Data from The Cancer Genome Atlas suggests a correlation between high EXOSC5 mRNA expression and unfavorable EC prognosis. EXOSC5 knockdown diminished EC-CSC self-renewal and reduced expression of key cancer stemness proteins, including c-MYC and SOX2. Intriguingly, this knockdown significantly curtailed tumorigenicity and CSC frequency in EC tumor spheres. A mechanistic examination revealed a reduction in netrin4 (NTN4) levels in EXOSC5-depleted EC cells. Moreover, NTN4 treatment amplified EC cell CSC activity and, when secreted, NTN4 partnered with integrin ß1, subsequently triggering the FAK/SRC axis to elevate c-MYC activity. A clear positive relation between EXOSC5 and NTN4 was evident in 93 EC tissues. In conclusion, EXOSC5 augments NTN4 expression, activating c-MYC via the integrin ß1/FAK/SRC pathway, offering potential avenues for EC diagnosis and treatment.

Biobased polyester versus synthetic fiberglass casts for treating stable upper limb fractures in children: a randomized controlled trial.

BACKGROUND: Stable upper limb fractures, such as radius, ulna, or distal humerus fractures, are common pediatric orthopedic traumas that are traditionally managed with cast immobilization. The commonly used synthetic fiberglass cast is light and water resistant but may promote skin itchiness during casting, which is a common complaint of patients. In addition, these diisocyanate-based casts have been proven to be toxic and may cause asthma. Herein, we introduce a novel biobased polyester cast to compare its clinical outcomes and patient satisfaction with conventional synthetic fiberglass casts. METHODS: From Feb 2022 to Nov 2022, we undertook a single-center prospective randomized trial involving 100 children with cast-immobilized stable upper limb fractures. These patients were randomized into either biobased polyester or synthetic fiberglass groups. All patients were regularly followed up till the cast removal which occurred approximately 3-4 weeks after immobilizing. Objective clinical findings and subjective patient questionnaire were all collected and analyzed. RESULTS: According to the radiographs taken on the day of cast removal, there was no loss of reduction in both groups. The incidence of skin problems was 3.4 times higher in the synthetic fiberglass group than in the biobased polyester group. For the subjective questionnaire, the biobased polyester cast was preferred in every sub-item. CONCLUSIONS: Our study strongly suggested that the novel biobased polyester cast provides matching stability to conventional fiberglass casts and improves patient satisfaction in an eco-friendlier and safer way. TRIAL REGISTRATION: ClinicalTrials.gov Protocol Registration and Results System ( https://www. CLINICALTRIALS: gov/ ; ID: NCT06102603; Date: 26/10/2023).

Tailoring Interlayer Chiral Exchange by Azimuthal Symmetry Engineering.

Recent theoretical and experimental studies of the interlayer Dzyaloshinskii-Moriya interaction (DMI) have sparked great interest in its implementation into practical magnetic random-access memory (MRAM) devices, due to its capability to mediate long-range chiral spin textures. So far, experimental reports focused on the observation of interlayer DMI, leaving the development of strategies to control interlayer DMI's magnitude unaddressed. Here, we introduce an azimuthal symmetry engineering protocol capable of additive/subtractive tuning of interlayer DMI through the control of wedge deposition of separate layers and demonstrate its capability to mediate field-free spin-orbit torque (SOT) magnetization switching in both orthogonally magnetized and synthetic antiferromagnetically coupled systems. Furthermore, we showcase that the spatial inhomogeneity brought about by wedge deposition can be suppressed by specific azimuthal engineering design, ideal for practical implementation. Our findings provide guidelines for effective manipulations of interlayer DMI strength, beneficial for the future design of SOT-MRAM or other spintronic devices utilizing interlayer DMI.

Efficacy of Combination Therapy (Hydrodilatation and Subdeltoid Bursa Injection With Corticosteroid, Mobilization, and Physical Therapy) vs Physical Therapy Alone for Treating Frozen Shoulder: A Randomized Single-Blind Controlled Trial, Phase I.

OBJECTIVE: To compare the efficacy of combination therapy (hydrodilatation and subdeltoid bursa injection with corticosteroid, mobilization, and physical therapy [PT]) with that of PT alone for treating frozen shoulder. DESIGN: A prospective, 2-arm parallel, single-blinded, randomized controlled trial. SETTING: Rehabilitation clinic of a private academic hospital. PARTICIPANTS: Patients (n=70) with frozen shoulder (freezing stage). INTERVENTIONS: Participants (n=35) in the combination group underwent hydrodilatation and subdeltoid bursa injection with corticosteroid twice, mobilization, and usual-care PT for 8 weeks; participants (n=35) in the PT group received only the usual-care PT for 8 weeks. MAIN OUTCOME MEASURES: The Shoulder Pain and Disability Index (SPADI) was the primary outcome measure. The secondary outcome measures were pain scores on a visual analog scale, range of motion (ROM), the Shoulder Disability Questionnaire (SDQ), quality of life (evaluated using the 36-item Short-Form Health Survey [SF-36]), and self-assessment of the treatment effect. RESULTS: Compared with the PT group, the combination group had significantly better pain (during activity), SPADI, SDQ, active and passive ROM, and self-assessment scores (all P<.001) as well as scores on some parts of the SF-36 (physical function and bodily pain, P<.05). Between-group differences were significant at the 1-, 2-, 4-, and 6-month follow-ups. CONCLUSIONS: A combination of hydrodilatation (with corticosteroid), bursal corticosteroid injection, and joint mobilization with PT was superior to PT alone for treating frozen shoulder, and the effects persisted for at least 6 months.

Segment anything model for medical images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging because of the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks. We comprehensively analyzed different models and strategies on the so-called COSMOS 1050K dataset. Our findings mainly include the following: (1) SAM showed remarkable performance in some specific objects but was unstable, imperfect, or even totally failed in other situations. (2) SAM with the large ViT-H showed better overall performance than that with the small ViT-B. (3) SAM performed better with manual hints, especially box, than the Everything mode. (4) SAM could help human annotation with high labeling quality and less time. (5) SAM was sensitive to the randomness in the center point and tight box prompts, and may suffer from a serious performance drop. (6) SAM performed better than interactive methods with one or a few points, but will be outpaced as the number of points increases. (7) SAM's performance correlated to different factors, including boundary complexity, intensity differences, etc. (8) Finetuning the SAM on specific medical tasks could improve its average DICE performance by 4.39% and 6.68% for ViT-B and ViT-H, respectively. Codes and models are available at: https://github.com/yuhoo0302/Segment-Anything-Model-for-Medical-Images. We hope that this comprehensive report can help researchers explore the potential of SAM applications in MIS, and guide how to appropriately use and develop SAM.