Resource Recovery from Waste Electronics has emerged as one of the most imperative processes due to its pressing challenges all over the world. The Printed Circuit Board (PCB) is one of the typical E-waste components that comprise large varieties of metals and nonmetals. Urban Mining of these metals has received major attention all over the world. The existing treatment procedures used extensively for the resource extraction are hydrometallurgy and pyro-metallurgy and crude recycling practices in the informal sector. However, these methods are prone to cause secondary pollutants with certain drawbacks. Also, the existing informal recycling procedures resulted in insignificant occupational health hazards and severe environmental threats. The application of biotechnology is extensively exploited for metal extraction and emerged as one of the sustainable and eco-friendly tools. However, a limited field-scale study is prevailing in the realm of resource recovery from E-waste using bioleaching method. Hence, the application of bioleaching requires more attention and technical know-how in developing countries to curtail crude practices. The application of bioleaching in E-waste, including its available methods, kinetics mechanism associated opportunities, and barriers, have been discussed in this paper. A glance of E-waste management in India and the menace of 95% crude E-waste recycling are also elaborated. The incentives toward profit, socio-economic, and environmentally sustainable approaches have been delineated based on critical analysis of the available literature.  The transformation to digital society is also evident from the article by Mckinsey and Company “Manufacturing’s next act 2015 ,4. Amongntil now . Of the ntil now . Based ontil now . China intil now ,11for prntil now –12. E-wantil now ,14. Howentil now . When thntil now . The Eurntil now . Therefoi.e., Bioleaching.Since E-waste is one of the secondary resources for many metals and nonmetals, it is recognized as a potential source for extended business opportunities. As estimated, the global E-waste generation is highly in demand when subjected to appropriate treatment options enables toward the economic growth of the country and reduces the need for mining virgin raw materials. However, it’s very saddening that only 15% of the E-waste is only subjected for valuable extraction . Hence, 1.1.i.e., 44.7 Mt, consists of approximately €55 billion worth of precious and other valuable metals [‘Urban mining is the concept and an approach for up-keeping the environment and ensuring the advancement of resource conservation through 3 R  of valuable and precious materials from waste’ . Among ve metals ,21. Urbae metals . Accordie metals . Also, ae metals . Accordie metals . The Proe metals , E-wasteThe specified targets that linked to the E-waste management are mentioned hereunder .SDG Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water, and soil pollution and contamination;SDG Target 8.3: Promote development-oriented policies that support productive activities, decent job creation, entrepreneurship, creativity and innovation, and encourage the formalization and growth of micro-, small- and medium-sized enterprises, including through access to financial services;SDG Target 8.8: Protect labor rights and promote safe and secure working environments for all workers, including migrant workers, in particular women migrants, and those in precarious employment;SDG Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying particular attention to air quality and municipal and other waste management;SDG Target 12.4: By 2020, achieve the environmentally sound management of chemicals and all wastes throughout their life cycle, in accordance with agreed international frameworks, and significantly reduce their release to air, water and soil in order to minimize their adverse impacts on human health and the environment;SDG Target 12.5: By 2030, substantially reduce waste generation through prevention, reduction, repair, recycling and reuse. Mining E-waste has envisaged toward reduced Co2emission approximately less than 80% per unit of gold to that of primary excavation and process.Bioleaching Technology. Also, a better understanding of the process, technical know how and additional suggestions are required for its implementation in developing countries. Based on the above context, extensive literature has been reviewed as a title search in the ‘Bioleaching: an effective approach for E-waste recycling’ from different sources. Reports published by the several agencies, such as Global E-waste Monitor, Toxic Link, India, ASSOCHAM, Despite tremendous advancement and research explored toward resource recovery from E-waste, still many countries are at the verge in handling the perils of E-waste. This paper is focused to get an outlook that India and many other developing countries have an immense potential to curb the E-waste hazards through implementing an integrated sustainable and cost-effective approach. Hence, considering the colossal menace of the E-waste generation all over the world and the existing treatment options, it has critically become mandatory to curb the threat of the E-waste in an eco-friendly way and further scaling up the 1.2.i.e., FR-4 and FR-2. FR-4 PCB forms the multilayer structure coated with a copper layer on fiberglass embedded through epoxy resin. These sorts are commonly utilized in small devices whereas FR-2 is made of a single layer coated with copper on a cellulose fibers board or glass layer, utilized enormously in large household appliances. A typical composition of PCB comprises 0.3% to 0.4%of precious elements , 28% of valuable elements  and toxic metals, such as lead, cadmium, arsenic and antimony including 49% of glass & ceramic and 19% plastics [Almost all the entire E-devices are snuggled with PCB implanted in complicated structure and design ensures the smooth functioning and conductivity of the gadget .PCB reprplastics . These mplastics ,35. As eplastics ,37. PCBsplastics . Hence, 2.viz., acid bath, and chemical leaching [viz., roasting, pyrolysis, burning, heating, smelting [Resource recovery from PCB’s is of great economic interest and a potential hotspot for urban mining of valuable and precious metals, such as copper, gold, silver, nickel, palladium and platinum, iron. It is also highly appreciable due to its advanced degree of purity over metals extracted from ores. Approximately 34 kg of gold can be retrieved by recycling one million cell phones . The resleaching , pyrometsmelting –43, and smelting –46 are dsmelting ,48. Alsosmelting –51.Figu2.1.3) [2SO4) on industrial scale, aqua regia and few alkalis followed by pretreatment of the waste [Hydrometallurgy also known as chemical leaching process involves dissolving the waste in appropriate solvents or acids or ammoniacal solutions , such as3) , hydrochhe waste . The prohe waste ,68. Thishe waste .The process is considered to consume a high amount of energy ,70 and r2.2.The process involves the separation of precious and base metals from inerts material embedded in WEEE. To ensure its efficiency, high-temperature melting furnaces are used in conjunction with special melting fondants. Few leading companies, such as Umicore, Dowa, X-Strata and Boliden are practicing pyrometallurgy for resource recovery . UmicoreHowever, the conventional methods of treatment are deliberated to be environmentally unsustainable, highly expensive, requires vast infrastructure including maintenance cost of the equipment’s and energy consumption. The comparative assessment of the treatment methods and criteria index adopted for resource recovery emphasizing its advantages and disadvantages has been shown in 2.3.i.e., 15 million people of total population living in the urban areas in most of developing countries are engaged in the informal sector [As estimated, approximately 1% l sector . In mostl sector . And thel sector . Therefo3.Occupational Health & Safety (OHAS), the ‘Occupational illness is a situation resulted due to continuous acquaintance in a typical occupation or profession subjected by any means of agent i.e., physically, chemically or biologically to an extreme that the standard metabolic functional mechanisms gets affected, and the condition of the person starts deteriorating’ [An occupational hazard is associated with the occupational risk to the person/workers which may have a serious potential impact on the health of the person. Similarly, occupational health hazard defines the harm that has occurred to the worker in the profession and that has taken the form of illness or risks to life. According to the orating’ . The potThe people involved in the occupation are exposed to various diseases, such as nausea, vomiting, reduced immune systems due to deposition of arsenic and other heavy metals in the blood vessels; fragile bones, lung damage, and kidney diseases due to cadmium, asthma, skin ulcers, severe redness; and swelling of skin due to chromium, brain and kidney damage may be death, pregnant women exposed to lead may cause miscarriage due to lead. Assumed routes of entry of metals led to hazard presented in the section.Routes of entryIngestion – Most of the time, the workers engaged in this profession inadvertently get exposed to toxic chemicals through the routes of mouth or swallowed (while consuming any eatables). Most of the emissions that are soluble prone to be more dangerous as they get absorbed in the intestine and can cause the internal damaging of the organs.Absorption – Since most of the activities during handling the E-waste is done without any prior protection equipment’s and mask. Also, most of the segregation, dismantling, and mechanical separation are done with bare hands the skin gets in contact with the toxic chemicals while processing the E-waste, most of the chemicals get absorbed in the skin and then into the blood system. As reported, most of the E-waste recyclers have reported suffering from skin diseases, such as reddening and itching of the skin.Inhalation – Most of the time while processing the E-waste, the toxic gases are emitted and inhaled in the form of vapors, gases, mists or particulates and get deposited in the respiratory tract as a result, the airborne contaminant can be easily absorbed through tissue. A personal interview was conducted by the first author on 12 May 2019 in Mumbai Manish Market a biggest electronic gadget, and repair in whole sale reported to inhale a chemical typically called as ‘FLUX’ every time the nozzle is opened.There are three primary routes of entry of toxic material into the body.3 of water is contaminated with cadmium by recycling the battery of a single mobile phone. For extraction of gold and silver, the recyclers often use an acid bath to dissolve the waste material & other metals and their remaining parts are washed and dumped directly into the nearby waterbodies. With the span of the time due to chemical reaction and certain changes in the landfills, the toxic elements start leaching contaminating the surface and groundwater pollution along with exacerbating the high levels of heavy metals in the nearby areas and surrounding land. The workers in rural areas are unaware of the impact of the contamination of drinking water and surface water with heavy metals.The burning of E-waste components, such as CRT, cable wires, plastics, PCB’s printed wiring boards (PWB’s) etc. emits toxic gases, such as furans and dioxin a colorless, flammable, highly volatile liquid and carcinogenic . These bIn purview of the above illustrated critical scenario, various associations, Government bodies, Environment Protection Ministries, Pollution Control Boards, Urban Local Bodies, Municipal Corporations Research and Development Institutions, NGO’s are working immensely to curb the menace of E-waste. Few of them are UNU Germany, ProSUM Project and Solving the E-waste Problem (StEP) Initiative have observed the menace of the E-waste and are constantly involved to curtail the perils of the hazards of the E-waste. StEP initiative has developed a concept called Best -of-2-Worlds (Bo2 W) that is focused to merge the technical as well as logical aspects for the advanced global best practices and processing facilities for E-waste. Also, a StEP initiative delineated that dismantling is one of the highly efficient and economically viable options to recover metals from E-waste. However, the components, such as PCB and the batteries waste would demand high tech technologies for resource recovery ,85,86.4.2 emissions thereby contaminating air quality, groundwater and soil quality. Therefore, the bioleaching method has desired to be sustainable technique with lower energy costs and improved environmental legacies [Acidithiobacillus ferroxidans (mesophilic-chemolithoautotrophic bacteria) [Sulphobacillusthermo sulfidooxidans and Thermoplasma acidophilum (acidophilic moderately thermophilic bacteria) [Chromobacterium violaceumanResource recovery from mineral ores using oxidizing bacteria is historically ancient civilizations in Asia . Based olegacies . It alsolegacies ,90. Althlegacies . Few micacteria) ,92. Sulpacteria) ,94 and Ci.e., sulphide mineral, bacteria and high redox potential’. The mechanism of the bioleaching process is elucidated by the estimation of O2 and CO2 consumed proportions coupled with the estimation of redox potentials and it also assist the kinetics of the sub-processes involved during the leaching [i.e., chemical leaching of the pyrite by ferric iron and the other process is the ferrous iron product is oxidized to ferric ion under the influence of the microbes. The kinetic study is applied to determine the ratio between the ferrous to ferric [i.e., direct bacterial leaching and indirect bacterial leaching. The dissolution of the mineral is basically accompanied by one or several mechanisms, such as complexolysis, acidolysis and redoxolysis [Bioleaching works on the principle of the ability of the metals to get dissolved by the oxidizing microorganisms . Accordileaching . The proo ferric  under opo ferric . Bioleacoxolysis  dependinoxolysis –101 as sPost bioleaching process the extraction of metals is a crucial and complex procedure. Due to no. of metals and nonmetals embedded over the waste material, a multicomponent solution is obtained. Therefore, the extraction of target metals is a complicated process. Various methods have been explored, such as electrowinning, electroplating and solvent extraction process from the leached solvents. According to Gotfryd and Pietek, 2013  and Pana4.2.In this process, the microorganism binds with the base of the waste and oxidizes the element followed by several enzymatically catalyzed steps as given in Also, direct bacterial leaching can be described as per the reaction given in 4.2.1.i.e., the microorganism (the mother culture) is grown in a flask in a aseptic condition and once the population count of the cells reaches to maximum i.e., exponential phase, the same is taken for the bioleaching as inoculum. Further, the inoculum is added into another flask that consists of the nutrient medium including E-waste sample [0 form to soluble form Cu2+using iron as an energy source , Chemical  and Biological  in E-waste bioleaching is critically important [To understand the technical knowhow toward maximum resource recovery, the evaluation of the potential influential factorsmportant .i.e., higher the pulp density, higher the chances of the metal recovery. However, at a certain level, the pulp density has assumed to have inhibitory effects on the microbial population count, such as in one step leaching process, the elevated pulp density inhibits the growth of the microbes due to release of heavy metals and ultimately reducing the leaching performance, slow agitation and limiting the mass-transfer [An ideal temperature for any microorganism is a highly influential parameter. As investigated so far, an ideal temperature for the growth of microorganism and their metabolic activity range widely between 25°C and 75°C. The average temperatures for microbial growth involved in bioleaching of E-waste are mostly mesophilic, moderate thermophilic and thermophilic. According to Indian climatic conditions and seasonal variation , the temtransfer . Hence, transfer . In additransfer recommendtransfer  large sipH is one of the most important factors that enhance the growth of the micro-organisms and their ability to solubilize the metals from the waste material. Based on various studies and research [Aspergillus niger produces different organic acids [research , microorresearch . Resourcresearch  includinic acids  at diffeic acids .Acidithiobacillus ferroxidans, Letospririllum ferroxidans and other heterotrophic microbes, such as species of Sulphobacillus [2+ to derive energy and solubilizes the metals. Hence, iron and sulfur compounds coupled with ammonium, phosphate and magnesium salts are supplemented for optimum growth conditions. One of the most common nutrient medium for microorganism used follows the Silverman and Lundgren known as 9 K medium that comprises,3.0 g (NH4)2SO4, 0.1 g KCl, 0.5 g MgSO4•7H2O, 0.5 g K2HPO4, 0.01 g Ca(NO3)2, and 74.5 g FeSO4•7H2O in 1 L distilled water [Despite all the above factors, the nutrient for the growth of the bacteria assumes a very important and significant role for the enhanced population count of the microbes. Among the broader range of microorganisms, the most commonly used microorganisms for bioleaching are acidophilus and chemolithotrophic microbial consortia ofbacillus . The Graed water . Despiteed water , bioleac4.4.1.G. Oxydans with 10–15 mM gluconic acid has been more effective. This indicates that the supernatant of microbial culture contributes toward higher yield of the metals leaching. As reported by Isildar [According to Pant et al., 2012 , usually Isildar , the bio5.i.e., collection, storage, transportation, treatment or recycling, the process are yet to be well regulated. As estimated approximately 70% of the E-waste in India is computer waste followed by 13% telecommunication equipment, 8% electrical equipment and 7% medical equipment [India being one of the most emerging and largest developing countries with the second largest populated country in the world has reported to generate 2.1 Mt of E-waste in 2019 . A studyquipment . The PCBquipment . Hence, quipment . Most ofquipment . Hence, quipment . Thus, B6.Environmental concerns and the need for resource recycling have made the E-waste processing an economically viable option and are required to make this process more efficient and economic. Significantly, bioleaching has boomed most of the recyclers to recover valuable and precious metals from E-waste. Hence, bioleaching presents as an advanced technology over pyrometallurgy and hydrometallurgy. It also aims to be beneficial to industries, small units engaged in waste management activities, Central and State Pollution Control Boards besides providing jobs to skilled and semi-skilled educational youth. The resources recovered from E-waste can be used as a circular economy approach by the industries thereby reducing the burden of raw material for manufacturing other products. Resource recovery using bioleaching technology has remarkable benefits on economic growth, public health and sustainable environment. It has gained much attention due to its simpler and cost-effective approach, reduced energy requirement, and negligible contaminations. However, the process is constrained to have certain limitations, such as slow& minimum yielding outputs, space requirement and proper infrastructure and current inadequacy for treating the complicated complex mixture of metals. The technology is associated with two broad aspects of sustainability-related to the environment, resource & health protection and profitability related to the value & utilization of recovered products. However, based on the existing scenario in the developing countries, there is a requirement of decentralized approach on a priority basis for improvement in E-waste management from cradle to grave. Also, advancement in the technologies and overcoming the limitation in the existing treatment options for more sustainable resilient environment, health, safety and resource conservation is also very much needed.Despite rapid advancement in the technologies and treatment procedures, each of the method has its own challenges and opportunities. Also, due to the critical and complexity of the resources embedded in the E-waste, none of the procedure can be exploited individually. Hence, to curb the E-waste menace, firstly an entire E-waste value chain is needed to be explored to delineate a sustainable model with a focus toward an integrated approach. The resource recovery from E-waste using bioleaching method has been studied extensively and explored at a laboratory scale. However, the field-scale study is limited and at an infancy stage in India and other developing nations. For an instance, in India, the E-waste collection system is still very poor due to no. of reasons, such as data privacy, emotional attachments, etc. Also, like MSW collections systems, there is no separate collection of E-waste items. However, few initiatives have been taken by the NGO’s as E-waste collection drive programs. Also, India is struggling with 95% of the E-waste in the informal sector and only 5% of the E-waste goes under the formal unit. India and many other developing countries are having a large scope in the field-scale implementation of the bioleaching method due to the availability of the E-waste material in a bulk amount. However, this is only possible with a sustainable and integrated model approach. The study is being conducted by the first author (a Ph. D. Scholar) addressing the above issues.


However, the existing methods for brucellosis diagnosis are time-consuming and expensive as they require a tedious experimental procedure and a sophisticated experimental device and performance. To overcome these defects, it is truly necessary to establish a real-time, on-site, and rapid detection method for human brucellosis. Here, a lateral flow immunoassay (LFIA) with a rapid, sensitive, and alternative diagnostic procedure for human brucellosis with a high degree of accuracy was developed based on blue silica nanoparticles (SiNPs), Staphylococcal protein A (SPA), and surface Lipopolysaccharide of Brucella spp. (LPS), which can be applied for rapid and feasible detection of human brucellosis. To our knowledge, this is the first report that uses blue SiNPs as a signal probe of LFIA for the rapid diagnosis of human brucellosis. The precursor of blue SiNPs@SPA such as colorless SiNPs and blue SiNPs was synthesized at first and then coupled with SPA onto the surface of blue SiNPs by covalent bond to prepare blue SiNPs@SPA as a capture signal to catch the antibody in the brucellosis-positive serum. When SPA was combined with the antibodies in the brucellosis-positive serum, it was captured by LPS on the test line, forming an antigen–antibody sandwich structure, resulting in the T line turning blue. Finally, the results showed that it is acceptable to use blue SiNPs as visible labels of LFIA, and standard brucellosis serum  could be detected at a dilution of 10−5 and the detection limit of this method was 0.01 IU/ml. Moreover, it also demonstrated good specificity and accuracy for the detection of real human serum samples. Above all, the blue SiNPs-based LFIA that we developed provides a rapid, highly accurate, and inexpensive on-site diagnosis of human brucellosis, and shows great promise in clinical diagnostics for other diseases.Brucellosis is a highly contagious zoonosis chronic infectious disease with a strong latent capability to endanger human health and economic development  A lateral flow immunoassay (LFIA) is a fast, simple, and economic method that is widely used in the field of rapid detection of various analytes. The labels used for LFIA include a series of particles, such as quantum dots , 2, lateBrucella spp. is a type of intracellular parasitic and facultative anaerobic Gram-negative short bacilli. According to the different susceptible hosts, Brucella spp. can be divided into six classical types, namely, Brucella abortus, Brucella melitensis, Brucella ovis, Brucella canis, Brucella neotomae, and Brucella suis  and Brucella ceti   , as well in 2010 , and ano in 2012 . The disd to 80% . Brucellery year . The maiStaphylococcal protein A (SPA), and surface lipopolysaccharide of Brucella spp. (LPS). At first, the precursor of blue SiNPs such as C.I. Reactive Blue 21 and colorless SiNPs was synthesized, and then C.I. Reactive Blue 21 was introduced onto the surface of colorless SiNPs in a given of amount of coupling substance to prepare blue SiNPs, followed by a layer of silica shell, which was formed by TEOS hydrolysis that was wrapped outside of SiNPs to avoid dye leakage. Afterwards, the amino group, which aims to combine with SPA by covalent bonding, was introduced to the surface of core-shell blue SiNPs via 3-aminopropyl triethoxysilane (APTES). After that, glutaraldehyde was used to activate the amino groups on the surface of blue SiNPs, and then SPA was added into above solution to obtain the blue SiNPs@SPA. The blue SiNPs@SPA as capture signal to catch the antibody in the brucellosis-positive serum. When SPA was combined with the antibodies in the brucellosis-positive serum, it was captured by LPS on the test line, forming an antigen–antibody sandwich structure, resulting in the T line turning blue. On this basis, the feasibility of this detection in human serum was also verified. We also discussed the feasibility of this work in human serum, and the results showed that it is acceptable to use blue SiNPs as visible labels of LFIA to diagnose human brucellosis in standard brucellosis serum and real human serum samples. Above all, a fast, one-step strategy and high-specificity method based on blue SiNPs for the diagnosis of human brucellosis has been developed, which can be applied in developing countries and rural areas.As little work using blue SiNPs as a reporter of LFIA to detect human brucellosis has been reported, here, an LFIA with a rapid, sensitive, and alternative diagnostic procedure for human brucellosis with a high degree of accuracy was developed based on blue SiNPs, SPA and goat anti-mouse IgG were provided by Beijing Bioss Biotechnology Co., Ltd. and (3-Aminopropyl) triethoxysilane (APTES) was provided by Shanghai Aladdin Bio-Chem Technology Co., LTD. Glutaraldehyde was obtained from Sigma-Aldrich ; bovine serum albumin (BSA) and tetraethyl orthosilicate (TEOS) were obtained from Shanghai Macklin Biochemical Co., Ltd. Anhydrous ethanol was obtained from Tianjin Xinbote Chemistry Co., Ltd. . Twenty five percent ammonia was purchased from Xilong Science Co., Ltd. Reagents used in indirect ELISA were purchased from Shanghai Jianglai Biotechnology Co., Ltd; phosphate buffered saline (PBS) buffers (pH 7.4) were obtained from Sangon Biotechnology Co., Ltd. . The glass fiber, releasing pad, nitrocellulose membrane, absorbing pad, and polyvinyl chloride sheet were obtained from Shanghai Jinbiao Biotechnology Co., Ltd. .Colorless SiNPs were synthesized by the Stöber method with a slight modification. A Stöber system containing 30 ml of anhydrous ethanol, 4 ml of water, and 5 ml of ammonia water was hydrolyzed for 30 min at 60°C. Then, 5 ml of TEOS was added slowly within 30 min, and the reaction continued for another 2 h. The final colorless SiNPs were isolated by centrifugation  and finally freeze-dried by a Lyophilizer.Dye-coated SiNPs were prepared by following a previous report with a slight modification . Briefly2O, 0.2 ml of TEOS, and 0.1 ml of NH3·H2O was hydrolyzed for 20 min, and then 5 ml of the above blue SiNPs was poured into the above solution and reacted for 12 h. After the reaction, the core-shell blue SiNPs were centrifuged and dispersed in pure water.A Stöber system that contained 10 ml of ethanol, 815 μl of HOne hundred milligrams of core-shell blue SiNPs was dissolved in 20 ml of ethanol under ultrasonic conditions at room temperature for 30 min. After that, the above prepared core-shell blue SiNPs were transferred into a three-necked flask containing 80 ml of ethanol, and 100 μl of APTES was added and refluxed at 60°C for 7 h. The resulting amino-modified blue SiNPs were washed four times with water.The SPA was combined with core-shell blue SiNPs through covalent crosslinking with a slight modification ; 2.5 ml −1 of FT-IR. After the dry samples were mixed with KBr, the samples were scanned at a speed of 2 cm−1.SiNPs and core-shell blue SiNPs were characterized by a transmission electron microscope (TEM) (200 kV) and a Fourier infrared transform spectrometer (FT-IR). KBr was set as the scanning background, and wavenumbers were set to 500–3,500 cmThe structure of the core-shell blue SiNPs-LFIA is shown in B. melitensis), brucellosis-positive control serum (B. melitensis), and negative control serum were purchased from Shanghai Jianglai Biotechnology Co., Ltd. The Brucellosis Prevention and Control Base, Chinese Centers for Disease Control and Prevention  donated a total of 102 human serum samples from their three clinical groups, including 69 human brucellosis serum samples, 24 negative control serum samples from healthy individuals, and 9 serum samples infected with other bacteria. All the serum samples were stored in a −80°C refrigerator. The serum agglutination test (SAT), the gold standard diagnostic method for brucellosis, was run following the Health Industry Standards of the People's Republic of China: Diagnosis for brucellosis GB/T (WS 269-2019), and each sample was run with three parallel tests. Relevant information on the serum samples is presented in Standard brucellosis serum , and each sample was run with three parallel tests. iELISA was run according to the previous protocol.2, as shown in 2 showed strong infrared absorption bands in the region of 800–1,180 cm−1, corresponding to Si-O-Si on the silica surface. As shown in the FT-IR absorption spectra of blue SiNPs-NH2, 3,450 and 1,610 cm−1 were attributed to the N-H bonds of the amino group. The above results were comparable with the blue SiNPs being successfully wrapped with an amino-modified silica shell; therefore, it is reasonable to believe that APTES was successfully conjugated with blue SiNPs, and amino groups were successfully coupled on blue SiNPs.FT-IR was used to characterize whether the amino group was modified on the surface of SiNPs. FT-IR provided direct proof for the functionalization of amino groups on the SiNPs, because FT-IR can detect the stretching vibration of chemical bonds on the surface of blue SiNPs and blue SiNPs-NHA TEM method was used to characterize the synthesized SiNPs. The TEM images of the colorless SiNPs and core-shell blue SiNPs are shown in Also, we tested the steadiness of blue SiNPs compared with colloidal gold nanoparticles in different pH values and different concentrations of salt solution . The resLFIA was employed to identify that SPA-conjugated core-shell blue SiNPs were successfully synthesized. The results could be observed by the naked eye, and it was obvious that the SPA-conjugated core-shell blue SiNPs were captured by a secondary antibody, and it showed a strong blue color at the C line . In contTo achieve the optimal response condition, four factors that would affect the final results were investigated. First of all, the amount of dye used in the synthesis of blue SiNPs was optimized. When too much dye was used, it was dissolved in the solvent and made it difficult for the blue SiNPs to be coated on the shell, which had been hydrolyzed by TEOS; however, if only a small amount of dye was used, then the color was not bright enough. So, it is necessary to find a suitable amount of dye that can not only make the color of blue SiNPs bright enough, but also prevent interferences. From Secondly, the sprayed concentration of LPS on the T line was optimized. It was obvious that the concentration of LPS affected the binding ability of LPS with antibodies in standard brucellosis serum. It can be seen from Next, we optimized the dosage of goat anti-mouse IgG. The sprayed concentration of goat anti-mouse IgG on the C line of the test strip could affect the binding ability of goat anti-mouse IgG with SPA. The goat anti-mouse IgG was printed in different concentrations  on the control line. It can be seen from Finally, we optimized the dosage of SPA. The concentration of SPA that coated on the surface of blue SiNPs could also affect the binding ability between SPA and antibodies in standard brucellosis serum. Experimental studies on the binding ability between SPA and LPS under different concentrations of SPA were carried out, and it can be seen from −1 to 10−6 IU/ml) were mixed with blue SiNPs, the sensitivity of the core-shell blue SiNPs-based LFIA for Brucella antibody was obtained. From −5, the T line still showed a blue color. According to the above results, we can conclude that our LFIA could detect Brucella spp. antibody at a level of 10−5 (0.01 IU/ml) in serum with no false positives.After the abovementioned experiment condition optimization procedure, the core-shell blue SiNPs-based LFIA was evaluated by standard brucellosis serum under optimal conditions. After the series of standard brucellosis serum  to determine whether the serum could be diagnosed as positive or negative; SAT followed the Health Industry Standards of the People's Republic of China: Diagnosis for brucellosis GB/T (WS 269-2019) to guarantee that there were no false positives, and each sample was run with three parallel tests to ensure accuracy. Indeed, the LFIA that we developed could detect antibodies in serum, in the whole blood, and even in plasma. Usually in a typical LFIA, the complex matrix can make the NC membrane change colors, which leads to a high background, especially in whole blood. However, due to the bright color of the blue SiNPs, even if the NC membrane turns red, the results were also easy to observe. In particular, the SiNPs can combine with a variety of dyes and appear in a variety of colors; therefore, the SiNPs can be more widely applied in the detection technology.We applied our developed LFIA to detect 69 positive brucellosis serum samples, 24 negative brucellosis serum samples, and nine non-brucellosis serum samples infected with other bacteria samples, and all the serum samples were verified by SAT. The sensitivity, specificity, and accuracy of core-shell blue SiNPs-based LFIA were assessed completely. From the results, we can see that with our method, 61 positive brucellosis serum samples were identified as “positive” correctly, eight positive brucellosis serum samples were wrongly identified as “negative,” 2 negative serum samples were wrongly identified as positive, and 31 of the negative samples were identified as “negative” correctly, as described in Furthermore, we also compared our methods with iELISA to evaluate the sensitivity and specificity. Compared with our developed method, iELISA was also used to detect the same 102 serum samples, and the resulting ROC curve and the results of iELISA are shown in As a new nanomaterial, SiNPs have significant advantages, namely, stability and feasibility, compared with other nanoparticles. Based on their stability, bioavailability, colorfulness, sensitivity, and specificity, SiNPs have shown great promise in immunology , virus dThe original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Institutional Research Ethics Committee of Medicine, the School of Public Health, Jilin University, permit number: JLU2014-0303.2.4. The patients/participants provided their written informed consent to participate in this study.LG, JL, and KX conceived and designed the study. LG, DW, FL, and YZ performed the assay of experimental detection. YW, JZ, and LZ analyzed the data. LG drafted the manuscript. JL, JW, and XS provided constructive opinions and suggestions. KX, JL, and XS reviewed and made improvements in the manuscript. All authors have read and approved the final version of this manuscript.This work was financially supported by the Department of Education of Jilin Province, China , the National Natural Science Foundation of China (Grant Numbers: 82173572 and 81401721), the Science and Technology Development Bureau of Jilin Province, China (Grant Number: 2018010195JC), the Jilin Provincial Development and Reform Commission (Grant Number: 2020C038-7), the Jilin Provincial Health and Family Planning Commission (Grant Number: 2017J074), and the Graduate Innovation Fund of Jilin University (Grant Number: 101832020CX275).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The published literature linking diabetes mellitus (DM) to intracranial aneurysm (IA) ruptured has been controversial and limited by methodology. Thus, this study was performed to examine whether hyperglycemia control status is independently associated with single IA rupture in patients with DM.We conducted a cross-sectional study on two Chinese hospitals between January 2010 and November 2017. Medical records of 223 patients with single IA and DM were reviewed and analyzed. We used glycosylated hemoglobin (GHB) as the independent variable of interest, and the outcome variable was ruptured status of IA. Covariates included data on demographics, morphological parameters, lifestyle habits, clinical features, and comorbidities.Multivariable adjusted binary logistic regression and sensitivity analyses indicated that GHB was not associated with IA rupture . A nonlinear association between GHB and IA rupture was observed, whose inflection points were 5.5 and 8.9. The OR values  were 0.38 (0.16–0.9) at the range of 1.88–5.5% of GHB, 1.6  at the range of 5.5–8.9%, and 0.56 (0.06–5.34) at the range of 8.9–10.1, respectively.The independent correlation between GHB and risk of IA rupture presented is nonlinear. The good glycemic control in single IA patients with DM can reduce the risk of IA rupture, and vice versa. Previous animal studies have suggested that long-term hyperglycemia duration is associated with intracranial aneurysm (IA) pathogenesis –5. HowevGlycated hemoglobin  is a reliable indicator for reflecting long-term blood glucose control . GlycateWe performed a large-scale cross-sectional study to investigate the association between GHB and rupture risk in Chinese patients with single IA.This is a cross-sectional study. We used GHB as the independent variable of interest to explore whether it was independently associated with the rupture status of single IA .A total of 1,847 patients with single IA were non-selectively and consecutively collected between January 2010 and November 2017 at the Department of Neurosurgery of Zhujiang Hospital, Southern Medical University, Guangzhou city, China and the Department of Neurosurgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou City, China. Our inclusion criteria were: (1) patients diagnosed with IA by digital subtraction angiography (DSA); (2) patients with a clear history of diabetes , and (3) patients taking/not taking blood sugar control drugs. Our exclusion criteria were as follows: (1) patients with multiple IAs, (2) patients with feeding artery aneurysm-associated arteriovenous malformations; (3) patients with fusiform IA; (4) patients with dissecting aneurysms; (5) patients with previous subarachnoid hemorrhage (SAH) history. After screening with the above inclusion and exclusion criteria, 223 patients with IA were left for data analysis  and sex ;morphological factors , 10: posother risk factors examined associated with rupture of IA –19: cererisk factors according to our clinical experience: coronary artery disease , current smoker , current alcohol consumption , and duration time of diabetes (year).2 , and performed Student t-test  or Mann-Whitney U-test (skewed distribution) to test for differences between the non-rupture and rupture IA groups.We presented continuous variables in two forms. In the first form, we expressed continuous variables with normal distribution as mean ± standard deviation. In the second form, we presented continuous variables with skewed distribution as medium . Categorical variables were expressed in frequency or percentage. We used χAddressing linear relationship: univariate and multivariate binary logistic regressions were employed. We constructed two models: model 1 (no covariates were adjusted), and model 2  was conducted. If nonlinearity was detected, we calculated the inflection point first using a recursive algorithm and then constructed a two-piecewise binary logistic regression model on both sides of the inflection point. In the end, the model that was more suitable for fitting (standard binary logistic regression model vs. two-piecewise model) the association between the target independent variable and the outcome variable was mainly determined by log likelihood ratio test.To ensure the robustness of the data analysis, we did a series of sensitivity analyses including:we converted GHB into a categorical variable according to quartile, and calculated the P for trend. The purpose was to verify the results of GHB as the continuous variable and to observe the possibility of nonlinearity.Due to differences in baseline characteristics, propensity score (PS) matching was performed to ensure that patients with ruptured IA and those with unruptured IA had similar baseline characteristics , 21. PS We considered that some eligible patients were unmatched during the matching process. Therefore, to prevent selection bias caused by patient lost, additional exploratory analyses by inverse-probability treatment weighting (IPTW) based on PS were performed. The equation of weight was as follows: when patients have no ruptured IA, weight = 1/(1–PS), and when patients have a ruptured IA, weight = 1/PS. Weighted binary logistic regression models were, therefore, used to estimate odds ratio (OR) and 95% confidential interval (CI) .It was noted that the purpose of both PS matching and IPTW using was only to verify the results of multivariate logistic regression (the matching variable is IA rupture), rather than in real-world research which were used for the purpose of post hoc randomization (the matching variable is exposure variable) .http://www.R-project.org, The R Foundation). P-values < 0.05 (two-sided) were considered statistically significant.All the analyses were performed with the statistical software package R . Compared to non-rupture group, participants in rupture group had lower aspect ratio (AR), and they more likely to be located in PcoA or MCA, reported less history of CAD, more female and longer duration time of diabetes. Besides, patients in the rupture group had a more irregular shape and wide neck than the those in the non-rupture group. In addition, we also present the variable distribution results after PS matching in In this study, a total of 84 cases were ruptured IA . P for the trend of GHB with categorical variables in the fully adjusted model was consistent with the result when GHB was handled as continuous variable. Besides, we also found that the trend of the OR among different GHB groups was non-equidistant. This non-equidistant variation of OR values indicates the possibility of nonlinearity.In this study, we constructed two models to analyze the independent association of GHB with risk of IA rupture with univariate and multivariate binary logistic regression models. OR and 95% CI are listed in In this study, we further explored the nonlinear relationship between GHB and rupture of IA . The smoIn summary, this analysis found a nonlinear correlation between IA rupture and GHB in Chinese patients with DM harboring single IA. Combining the trend of OR values in different GHB ranges and referring to the normal range of Chinese GHB (4–6%), our findings suggest that for Chinese patients with DM and single IA, good and stable glycemic control may be associated with reduced risk of IA rupture. Conversely, a high glycosylated hemoglobin ratio will significantly increase the risk of IA rupture.The association between DM and IA rupture risk has been confusing. Lindgren et al.  reviewedn = 4,701) reported by Can et al. . Conversely, in patients with poor glycemic control, GHB is positively associated with IA rupture risk, which indirectly confirms the results of Anil Can et al.; that is, use of hypoglycemic agents reduces the risk of IA rupture. However, this positive correlation has a saturation effect; that is, when GHB is >9, even if GHB increases again, the risk of IA rupture does not increase further.This research has several strengths of note. First, compared to most previous studies that only adjust morphological parameters or demographic characteristics, the adjustment strategy of this study is more complete and sufficient. Second, to our best knowledge, it is the first to address nonlinearity and test interaction on the association of GHB with IA rupture. Nonlinearity addressing makes our results have more clinical value. Third, the sensitivity analysis ensures the robustness of our findings. PS matching and IPTW are a good proof that our results regarding the linear association between GHB and IA rupture risk are robust.Indeed, interpretation of the findings of this study should be made with caution. Several limitations are also noteworthy. First, conclusions can be generalized to patients with single IA only, and correlation of GHB with ruptured IA may be different in multiple IAs. Second, limited to actual clinical conditions, this study was only designed as a cross-sectional one, so the causal association between GHB and IA rupture could not be confirmed. However, GHB reflects long-term blood glucose control and is better than fasting blood glucose . We can, therefore, think of GHB as one of the factors that influence the risk of IA rupture. Third, although we have adjusted for measurable confounders, as in all observational studies, there still might have been uncontrolled confounding due to unmeasured differences between IA with and IA without rupture. Although PSM and IPTW can weaken the impact of non-measured confounding factors on outcomes , they caFor patients with single IA and DM, good glycemic control can effectively reduce the risk of IA rupture, and the results are independent of known risk factors such as demographics, morphology, lifestyle, and comorbidities.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Zhujiang Hospital of Southern Medical College. The Ethics Committee waived the requirement of written informed consent for participation.X-FL completed the manuscript and participated in the study design. X-TW participated in data collection and data analysis. S-XS participated in research design and paper review. XZ provided research funding and participated in study design and manuscript writing. Y-MB participated in data collection. C-ZD participated in the study design and review of the paper. All authors contributed to the article and approved the submitted version.This study was supported by the National Natural Science Foundation Project (Grant No: 81974177) and the Initiation Plan Project of Clinical Research of Southern Medical University (Grant No: QD2018N022).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The purpose of this study was to assess the risk of occurrence of Parkinson's disease (PD) among subjects with type 2 diabetes and diabetic retinopathy (DR) in our large primary health care database from Catalonia (Spain). A retrospective cohort study with pseudo-anonymized routinely collected health data from SIDIAP was conducted from 2008 to 2016. We calculated the number of events, time to event, cumulative incidence, and incidence rates of PD for subjects with and without DR and for different stages of DR. The proportional hazards regression analysis was done to assess the probability of occurrence between DR and PD. In total, 26,453 type 2 diabetic subjects with DR were identified in the database, and 216,250 subjects without DR at inclusion. During the follow-up period, 1,748 PD events occurred. PD incidence rate and cumulative incidence were higher among subjects with DR . In the unadjusted analysis, subjects with DR were at 1.25 times higher risk  of developing PD during the study period. However, we did not find any statistically significant HR for DR in any models after adjusting for different risk factors . In conclusion, in our primary health care population database, DR was not associated with an increased risk of PD after adjusting for different risk factors. In our retrospective cohort study, age, male sex, and diabetes duration were independent risk factors for developing PD. Diabetes mellitus (DM) is a metabolic disorder that may result in an unfavorable impact in different organs and leading to numerous complications . The comParkinson's disease (PD) is the second most common neurodegenerative disease. Generally, this chronic disease is more prevalent among older adults and Hispanic people . So far,The results related to the association between DM and PD reported in meta-analyses have also been heterogeneous. In one meta-analysis with 11 observational studies (four cohort and seven case-control studies) on the association of diabetes as a risk factor for PD, the authors concluded that diabetes appears to be a risk factor for PD . AnotherEvidence suggests that DR and PD share similar pathophysiological characteristics and mechanisms  related to disrupted dopamine activity since both brain and retina express D1-like and D2-like dopamine receptors . The phoTo our knowledge, there is only one large population database study from South Korea aimed to investigate this association. The authors found that the incidence of PD was higher among DM subjects and even higher among subjects with DM and DR; however, they acknowledged that important variables related to the DM were not available for the analysis, such as duration of diabetes, and glycated hemoglobin . On thisWe used a retrospective cohort of subjects with T2DM attended in primary health care centers from the Catalonian Health Institute–ICS, using the pseudo-anonymized routinely collected health data from the SIDIAP  database. This database is a well-validated data source for the study of diabetes in Spain , 21, colWe included all subjects aged 30 years or above, with a register of T2DM defined as the presence of relevant ICD-10  diagnostic codes and sub-codes (E11 and E14). Subjects with diagnostic codes for other types of diabetes  or without T2DM codes were excluded from the analysis. Those subjects with preexisting primary and secondary Parkinson's disease (ICD-10: G20 and G21) were also excluded from the study population.2 and/or an albumin/creatinine ratio >30 mg/g. To identify cardiovascular disease, we used diagnostic codes alone. Moreover, concomitant medication , laboratory parameters , and clinical variables  were collected. The detailed information on the different codes used in defining the study variables is included as At inclusion, variables related to DR and clinical characteristics of the subjects were collected. DR was defined as the presence of diagnostic codes and sub-codes  and/or abnormal (pathologic) results for fundus photography. In those with available fundus photography data, DR was stratified in different stages using the Early Treatment Diabetic Retinopathy Study (ETDRS) classification: no apparent retinopathy (NDR), mild non-proliferative retinopathy (NPDR), moderate NPDR, severe NPDR, proliferative diabetic retinopathy (PRD), and diabetic macular edema (DME) . We alsoDuring follow-up, we collected data related to PD as a primary study event. PD was defined as the presence of a diagnostic code for Parkinson's disease (ICD-10: G20). The follow-up period was defined as the time between the inclusion in the study and the primary study event.For the variables collected at inclusion, we used descriptive statistics. The number and frequencies for the qualitative variables were calculated, while we estimated the means and standard deviation for the quantitative variables.p < 0.05. Additionally, adjusted HRs were calculated using different clinically important variables as risk factors. Gradually we added different risk factors to the model, starting with age and sex in the first model, adding T2DM duration, smoking status, hypertension, dyslipidemia, and BMI in the second model, and adding CKD, CVD, and HbA1c in the third model. Moreover, to assess the effect of diabetes duration and HbA1c on the association of DR with risk of incident PD, we performed additional models with different combinations of these two variables with the other relevant variables from the first model. We also performed a sensitivity analysis with the estimates from different models and, also, stratification for diabetes duration and HbA1c. Data management and all analyses were performed using R statistical software, version 3.6.1.At follow-up, for the primary study event (PD), we calculated the number of subjects and events for each group (presence/absence of DR and each stage of DR), time to event (time to a diagnosis of PD after a diagnosis of DR), cumulative incidence and incidence rates (person/year). To assess the probability of occurrence between DR and PD, we used proportional hazards regression analysis. The hazard ratios (HR) for the primary outcome event were calculated with corresponding 95% confidence intervals (CI), and statistical significance was established as a The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of IDIAP Jordi Gol i Gurina Foundation .From 2008 until 2016, 250,363 subjects with T2DM were identified in the SIDIAP database. We excluded 1,850 individuals who had a previous diagnostic category of PD and 5,838 individuals without T2DM diagnostic codes. In total, 216,250 subjects did not have DR at baseline, while 26,453 had DR by diagnostic code and/or diagnosis by fundus photography. The study flowchart is presented in The clinical characteristics of the cohort and different groups at inclusion are presented in Information on the time of follow-up, events, and cumulative incidence are presented in We observed statistically significant un-adjusted HR (Unadj-HR) for the primary study event between the groups . The sub2 and being an ex-smoker decreased the risk of PD. In the additional models, adjusting only for age, sex, and diabetes duration and/or HbA1c, we did not find a significant association of DR with PD. The age, sex and diabetes duration remained risk factors for PD in these models. In the sensitivity analysis, stratifying by diabetes duration or HbA1c, similar tendencies were observed for the HRs for DR observed in the previously described models. However, having an HbA1c between 9.1 and 10% and DR was negatively associated with PD when including additional relevant variables in this additional model  or variables in model 3 . The results of this sensitivity analysis are shown in The results from our retrospective cohort study from a primary care database from 2008 until 2016 showed no increased risk for developing PD among subjects with previous diabetic retinopathy in fully adjusted models. Instead, age, male gender, and longer T2DM duration conferred an increased risk of PD over time independently among T2DM subjects.2). The differences found between subjects with DR from the two studies are not surprising. It is well reported that the characteristics of eastern Asian people with T2DM are different from European T2DM subjects . Comparing groups without DR between the two studies, we observed a higher incidence rate than in the South Korea study . An explanation for this difference could be that our non-DR population was older than the South Korean population, and it is well known that PD increases rapidly with age  related to PD than previously published studies.In conclusion, in our primary health care population database, DR was not associated with an increased risk of PD after adjusting for different risk factors. The detection of DR is mainly based on retinal vascular changes that do not always coincide with retinal neurodegeneration . Even thdidacmauricio@gmail.com.The data analyzed in this study is subject to the following licenses/restrictions: restrictions apply to the availability of some or all data generated or analyzed during this study because they were used under license. The corresponding author will on request detail the restrictions and any conditions under which access to some data may be provided. Requests to access these datasets should be directed to Dídac Mauricio, The studies involving human participants were reviewed and approved by IDIAP Jordi Gol i Gurina Foundation . Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.DM: conceptualization and methodology. JR: software, formal analysis, and data curation. JR, BV, and DM: validation. XM-T: resources. BV: writing—original draft preparation. BV and DM: writing—review and editing. DM, JJ, JR, EO, EC, JB, JK, and JF-N: supervision. All authors have read and agreed to the published version of the manuscript.DM and JF-N received advisory and/or speaking fees from Astra-Zeneca. Ascensia. Boehringer Ingelheim. GSK. Lilly. MSD. Novartis. Novo Nordisk. and Sanofi and received research grants to the institution from Astra-Zeneca. GSK. Lilly. MSD. Novartis. Novo Nordisk. Sanofi. and Boehringer. EO has received advisory and or speaking fees from Astra-Zeneca, Boehringer Ingelheim, Lilly, MSD, Novo Nordisk, Sanofi, and Amgen; he has received research grants to the institution from MSD and Amgen. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated—whether as a subtype of depression, or as a distinct disorder altogethe—interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics. Depression and melancholia are synonymous terms in common speech, but have different technical meanings in psychiatry . While dSince then, there has been significant thrust on classifying melancholic depression. Because of the overlapping symptoms between depression and melancholia, researchers have been focusing on distinguishing the two. This distinction is important for both diagnosis and treatment, since melancholic patients  may have different responses to treatment . For exaIn the recent years, computational and statistical models have been used to differentiate melancholic from non-melancholic depression . The comOur research further explores the brain dynamics of melancholic depression. Several neuropsychiatric disorders are rooted in cognitive dysfunctions caused by disruptions in the dynamics of functional brain networks , 19. EviIn ELA, the brain is characterized as a dynamic system that switches to multiple stable states . Each stThis study examines the energy landscapes of melancholic depression. Here, we show that melancholic depression exhibits a distinct energy landscape, in which its switching dynamics will be different from that of non-melancholic and healthy brain networks. By building an energy landscape model for melancholic depression, we further delineate the boundary between melancholia and depression; not necessarily to set them apart, but rather to clear the obfuscations that currently entangle them.This study is a secondary analysis of rs-fMRI data of healthy and depressed volunteers recruited starting in 2012 from four medical institutions in Hiroshima, Japan . Prior tAdditional screening of participants was performed to emphasize the boundary between groups in the data set. First, the participants were screened based on their Beck Depression Inventory-II scores  . Second,In the end, a total of 142 healthy (71 F / 71 M) and 120 depressed (60 F / 60 M) participants were screened and selected for analysis. Within the depressed group, there were 89 melancholic (44 F / 45 M) and 31 non-melancholic (16 F / 15 M) participants. Details on their demographic data are summarized in We recorded resting-state functional Magnetic Resonance Imaging (rs-fMRI) data of the participants using gradient echo planar imaging (EPI) sequences. The imaging device and parameters differ depending on the recording site . During Data preprocessing for the rs-fMRI data was performed using SPM8  on Matlab . The preprocessing procedure included slice-timing correction, mean image realignment, normalization and resampling through segmentation of structural image aligned with the mean functional image, and smoothing with an isotropic 6 mm full-width half-maximum Gaussian kernel . The potWhen selecting the regions of interest (ROIs) for analysis, we considered functional brain networks that were associated with depression. Time series data were extracted from 74 ROIs according to the 90-ROI Shirer Brain Atlas . Some ROTo compare the brain dynamics of melancholic and non-melancholic participants, we implemented the Energy Landscape Analysis (ELA) method which utilizes the Pairwise Maximum Entropy Model (P-MEM)  36). In. In36). In ELA, we began by combining the rs-fMRI data of all participants in a group  to create group-level dynamics model. In each group, the concatenated time series data were extracted from the corresponding ROIs of the chosen functional network . Then, tn ROIs, there are 2n possible states. For each brain state σ, we computed the empirical probability p(σ),Binarizing the signals allowed us to encode the time signals into brain state sequences, wherein a brain state was defined by the activity pattern, i.e., active and inactive ROIs, at a given time . Given bnσ is the number of occurrences that the state σ appeared in the time series, and T is the total length of the time series . In P-MEM, the state energy E(σ) in Equation 2 was restricted in the quadratic form, and expressed as:where the sum in denominator is taken over all 2h and J, which leads to lower (more negative) energy (Equation 3), and higher occurrence probability (Equation 2) (with ation 2) .h & J by maximizing their likelihood, as defined byWe then built the P-MEM from the model parameters T.for the entire time series of length With the P-MEMs for healthy, non-melancholic and melancholic groups, we analyzed the energy landscapes and transition dynamics of heterogeneous depressed brain networks . The ELAEnergy Barriers, the energy barrier between states a and b are highlighted . Here, a → b has higher barrier, and thus has lower probability of occurring  are states with the lowest energy relative to their neighboring states. States are said to be neighbors if they differ in only one active/inactive region. If neighboring states have lower energy barriers relative to other states, they become part of the closest basin cluster . Basins ccurring .Energy Landscape Analysis, a 3D schematic diagram depicts the basins in an arbitrary state space. Here, basin clusters are plotted as concentric circles, where the basin is at the center, and neighboring states are on circles with radius equivalent to the their distance to the basin . Thus, each circle represented the states that were equidistant to the basin. Then for each circle (including the center), its depth was equivalent to the energy of the state with lowest energy (Equation 3) in that circle.Energy landscapes were then constructed to depict the energy level and cluster size of the basins. In f(A) on individual fMRI time series. The occurrence frequencies of all basins (major and minor) are then summed for each group and for each network.Since energy landscapes modeled only the group-level brain dynamics, we opted to analyze the brain dynamics of individual participants by computing the occurrence frequency of basins A1 and A2; with P1 as clustered states for A1, P2 for A2. Then we computed the following participant-level transition rates,We also analyzed the transition dynamics on the energy landscapes. For each network, we selected two major basins as the basins with the lowest energy, n(U → V) is the number of times the participant's time series entered state U and arrived at state V; TR(A → A′) is the major transition rate for major basins; TR(P → P′) is the peripheral transition rate for clusters of major basins. Moreover, we computed the staying rates of the states,where SR(A → A′)), or the periphery (SR(P → P′)). The transition rates were used to define the Traveling Score, which measured the amount of times the brain successfully traveled from one major basin to another . Similarly, the staying rates were used to define the Lingering Score, which measured the amount of times the brain lingered on a basin or along its periphery ,where this time we counted the number of times the participant's brain activity stayed on the major basins (Note that the traveling score (Equation 10) is also known as efficiency score, which is the index of ease of transitions .p < 0.05 for both types of statistical analyses. One-way ANOVA with Bonferroni correction was also applied to test for statistical significance of results when comparing individual- and group-level differences in energy landscape characteristics such as basin frequency and transition rates. These results were also verified by Kruskal-Wallis test to cope with non-normally distributed data as well.One-way Analysis of Variance (ANOVA) was performed to find significant group differences  in participants' age, IQ, BDI-II and SHAPS  scores. Bonferroni correction was applied to compensate for multiple group comparisons. Similarly, Chi-squared test was performed for group differences in sex. Group differences are presumed to be statistically significant at p < 0.05 deemed as statistically significant relationship between the variables.Finally, we investigated the correlation between depressive symptoms and basin characteristics. Two criteria were used for analyzing depressive symptoms: BDI-II for depression diagnosis , and SHAp < 0.05, Healthy vs. Melancholic; p < 0.001, Non−melancholic vs. Melancholic), followed by non-melancholic group . The results were consistent with many previous reports , Dorsal DMN (DDMN), and Left ECN (LECN) are shown in In both BGN and LECN, the non-melancholic and melancholic groups had deeper major basins than the healthy group. Remarkably, a different pattern can be observed in the DDMN, where the non-melancholic group had shallower major basins than the healthy and melancholic groups.Furthermore, some major basins were unique to specific groups. For example, non-melancholic group had a unique major basin in DDMN, where the left and right angular gyri , posterior cingulate cortex (PCC), and precuneus regions were activated. Similarly on LECN, the non-melancholic group also had a unique major basin, where left inferior frontal gyrus (LIFG), orbitofrontal gyrus (OFG), left inferior temporal gyrus (LITG), middle temporal gyrus (MTG), and left thalamus regions were activated. Complete results for basin energies are reported in Lastly, the major basin pairs had antisynchronized activations . In BGN,n = 6), followed by healthy group (n = 5), then non-melancholic group (n = 4). Because of this shortage of basins, the non-melancholic has relatively larger major basins (sA = 98.6%), compared to healthy (sA = 92.0%) and melancholic groups (sA = 89.4%). Note that, as mentioned previously, the non-melancholic group has a unique major basin (A2). Also, there is a hill on basin cluster B2 of the non-melancholic group, which may be inferred as highly unstable cluster of states. This hill state cluster is absent in the other groups. We compared the basin sizes (number of states clustered to the basin) of each group for each network but found only minimal differences . Complete results for basin sizes are reported in Energy landscapes are then constructed based on the basin information. In p < 0.005; H vs. M, N vs. M; Auditory Network). In other networks, an opposite, increasing trend appeared . And for the rest, the non-melancholic group had either the lowest  or highest frequency . Similar results for the other functional networks are summarized in Significant group differences were found on the basin frequencies across all selected networks. The most notable results are summarized in p < 0.05; N vs. M; DDMN), and lingering scores  of individual participants. There was also significant group differences in the lingering scores between healthy and non-melancholic , and healthy and melancholic groups . Furthermore, there is significant increase in lingering scores of melancholic group in LECN (Lingering = 0.61 ± 0.06) as compared to non-melancholic  and healthy groups . Results for other functional networks are summarized in Depression heterogeneity was also evident when comparing the transition dynamics in some networks. As shown in In this study, we characterized the brain dynamics of melancholic depression by analyzing the energy landscape models constructed from temporal rs-fMRI signals. The distinguishing energy landscape features of melancholic depression included major basin energy barriers, basin frequency, and state transition dynamics. Statistical results were consistent across our analyses on 12 functional brain networks, which indicates the robustness and discriminative power of our model. Moreover, these results agree with existing studies on melancholic features, such as depression severity , 40, 43,Our first goal was to show heterogeneity in our depressed participants. Depression heterogeneity has plagued research advancement on the diagnosis and treatment of the disorder –49. Consdepression with melancholic features)  , its clieatures) . Our anaeatures) ; basin features) ; and traeatures) .Next, we sought meaningful relationship between energy landscape signatures and depression severity. Melancholic depression is associated with greater symptom severity in comparison to depression with non-melancholic features , 43. ThiReaders may wonder if energy landscape features can be a proxy to evaluate some symptoms associated with melancholic features. To check this possibility, we performed regression analysis on basin frequencies and depression severity score (BDI-II). But despite having clear boundaries among the groups, no significant correlation were found in any of the networks .The order of increasing severity from healthy, to non-melancholic, to melancholic depression may seem intuitive. However, some studies argue against the severity-based categorization of melancholic depression , 53, andFollowing depression severity, we then tested if the correlation with basin frequency would also hold true for anhedonia. Anhedonia is one of the overlapping symptoms for both melancholia and depression , 42. ThidmPFC) with posterior cingulate cortex / precuneus (PCC/PCUN) is related to depression severity and anhedonia . This decoupling between mPFC and PCC/PCUN may be attributed to impaired reward anticipation, which is a key feature of anhedonia  (traveling score (Equation 10) for dynamic activity, and lingering score (Equation 11) for static activity. Thus, high traveling score and low lingering score would imply a more active network.Lastly, we investigated the possible presence of ruminative states in the energy landscapes. Rumination is the tendency to dwell on the same, usually negative, thoughts for prolonged periods of time. Currently, rumination is not recognized a diagnostic feature for melancholic depression. However, since past studies have noted the significant correlation of rumination to both melancholy  and deprto LECN) . In termDDMN), the lingering scores of melancholic group (Lingering = 0.55 ± 0.07) are significantly lower than healthy  and non-melancholic groups . Although this may support previous studies showing increased connectivity in DMN during ruminative thinking, analyzing the lingering scores in ventral part of DMN (VDMN) leads to contradicting results (Traveling = 0.10 ± 0.09) than in non-melancholic group . For LECN and VDMN, there were no significant differences in traveling scores between groups.The significant increase of lingering scores of the melancholic group in LECN  could imUnlike typical functional connectivity (FC) models that are based on correlation between two regions , the ELA model does not assume that pairwise interactions of regions are independent from each other . This alHidden Markov Model (HMM) is another common brain dynamics model for resting-state fMRI. In comparison to ELA which utilizes pairwise MEM, HMM is more complex, and may be more expressive . Thus, iFurthermore, our present study have other limitations. ELA assumes a memoryless process, such that predicting the next state only depends on the current state, and not the past states . This saAlthough concatenation of individual fMRI data should only have minimal effect to our analysis due to the memorylessness aspect of ELA , it shouOur selection of ROIs relies on the parcellation method  and avaIt is often pointed out that the site difference could be a potential confounding factor for the observed BOLD signals . In facta priori that the energy landscape of melancholic depression will be different from non-melancholic due to depression heterogeneity, our analyses and interpretations of the results were done post-hoc. For statistical analyses, we applied Bonferroni correction to compensate for multiple comparison tests that would increase the risk of Type 1 error  .Finally, we would like to emphasize that melancholia, in itself, is also heterogeneous . It is cMelancholic depression is a debilitating disorder that robs a person the pleasure and excitement from activities they used to enjoy. In this study, we developed an energy landscape model to better understand the brain dynamics involved in melancholic depression. Relative to healthy and non-melancholic groups, the melancholic group showed significant differences on basin energy, basin frequency, and transition dynamics in several functional networks. Moreover, possible connections were traced between major basins and depressive symptom scores such as depression severity and anhedonia. Taken together, these results suggest that melancholic depression is a distinct disorder, and thus should be diagnosed and treated with utmost precision and care.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of Hiroshima University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.PR: methodology, software, investigation, writing—original draft, and writing—review and editing. MT: resources and writing—review and editing. TN: conceptualization, supervision, and writing—review and editing. NI, AF, and GO: resources. YO and SY: supervision and funding acquisition. KI: supervision and writing—review and editing. JY: conceptualization, methodology, investigation, writing—original draft, writing—review and editing, and funding. All authors contributed to the article and approved the submitted version.This research was supported by the Strategic Research Program for Brain Sciences  from the Japan Agency for Medical Research and Development, AMED, grant nos. JP20dm0107093 and 20dm0107096. This work is also partially supported by JST-Mirai Program grant no. JPMJMI20D6, Japan; and JSPS KAKENHI grant nos. JP21H00949, JP21K07521, JP20H00625, and JP20K06874.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Among the functional limitations associated with hip osteoarthritis (OA), the alteration of gait capabilities represents one of the most invalidating as it may seriously compromise the quality of life of the affected individual. The use of quantitative techniques for human movement analysis has been found valuable in providing accurate and objective measures of kinematics and kinetics of gait in individuals with hip OA, but few studies have reported in-depth analyses of lower limb joint kinematics during gait and, in particular, there is a scarcity of data on interlimb symmetry. Such aspects were investigated in the present study which tested 11 individuals with hip OA (mean age 68.3 years) and 11 healthy controls age- and sex-matched, using 3D computerized gait analysis to perform point-by-point comparisons of the joint angle trends of hip, knee, and ankle. Angle-angle diagrams (cyclograms) were also built to compute several parameters  from which to assess the degree of interlimb symmetry. The results show that individuals with hip OA exhibit peculiar gait patterns characterized by severe modifications of the physiologic trend at hip level even in the unaffected limb , as well as minor  alterations at knee and ankle level. The symmetry analysis also revealed that the effect of the disease in terms of interlimb coordination is present at knee joint as well as hip, while the ankle joint appears relatively preserved from specific negative effects from this point of view. The availability of data on such kinematic adaptations may be useful in supporting the design of specific rehabilitative strategies during both preoperative and postoperative periods. Hip osteoarthritis (OA) is a progressive musculoskeletal disorder that affects approximately 7 to 13% of individuals aged over 60 years old in the general population . Hip OA Hip OA is associated with pain, stiffness, and functional limitation in several activities of daily living  includinHowever, despite the increasing interest in applying quantitative techniques to support clinical decisions (in terms of preoperative planning and postoperative care and rehabilitation), several aspects of gait kinematics of people with hip OA remain relatively less explored. In particular, only few studies report information on the joint kinematic alteration induced in the contralateral limb ,13,14 anA convenience sample of 11 individuals diagnosed with primary hip osteoarthritis (OA) currently followed at the Department of Orthopaedics of the “Nostra Signora di Bonaria” General Hospital  was recruited for the study on a voluntary basis. Osteoarthritis was classified according to Kellgren–Lawrence criteria  and all The study was conducted according to the principles expressed in the World Medical Association Declaration of Helsinki, and all participants signed a written informed consent form with detailed information about the aims of the study.The kinematics and spatio-temporal parameters of gait were assessed using a motion-capture system composed of 8 infrared cameras  set at a frequency of 120 Hz .Before the experimental tests, anthropometric data  were acquired, and 22 spherical retro-reflective passive markers were placed on subjects’ skin following the protocol described by Davis et al. . In partFive spatiotemporal gait parameters, namely gait speed, cadence, step length, step width, stance, swing, and double support phase duration;Nine kinematic parameters: pelvic tilt, rotation, and obliquity; hip flexion-extension, adduction-abduction, and rotation; knee flexion-extension, ankle dorsi-plantarflexion, and foot progression. From these, summary indexes of gait deviation from normality  were obThe dynamic range of motion (ROM) for hip and knee flexion-extension and ankle dorsi-plantarflexion. They were calculated as the difference between the maximum and minimum angle value recorded during the gait cycle;The kinematics of hip, knee, and ankle on the sagittal plane, i.e., hip and knee flexion-extension and ankle dorsi-plantarflexion angle variations during the gait cycle. Such data were also employed to calculate the interlimb symmetry parameters as described later in detail.Lower limb kinematics were recorded while the subjects walked at a self-selected speed in the most natural manner, along a 10 m long walkway for at least 6 times, providing sufficient rest time if needed. The raw data were first processed with the dedicated Smart Analyzer  software to calculate:2): the area enclosed by the curve obtained from each angle-angle diagram [Cyclogram area (degrees diagram . In the Cyclogram orientation (degrees): this parameter is expressed by the absolute value of angle ϕ formed by the 45° line (which corresponds to perfect interlimb symmetry) and the orientation of the principal axis of inertia, which corresponds to the minimum moment of inertia of the cyclogram ,22. The Trend Symmetry: this dimensionless parameter is calculated to assess the similarity between two waveforms  using an eigenvector analysis  gait speed, cadence, and step width (for which both limbs are involved) and (ii) stance, swing, double support phases, and step length, which can be analyzed distinguishing between limbs. The possible differences introduced by the presence of OA in spatiotemporal parameters were explored using a one-way multivariate analysis of variance (MANOVA) considering group (OA/CG) as the independent variable and the 3 parameters previously listed as dependent variables. In contrast, we ran a one-way MANOVA for the phase duration parameters considering the limb  as independent variables and the 4 parameters previously mentioned as dependent variables.For dynamic ROM, GVS, and GPS analysis, two separate MANOVAs were carried out considering the limb  as the independent variable and the three ROM or the 9 GVS and GPS as dependent variables.The possible differences in joint kinematics were assessed by comparing the “angle vs. time” curves of the affected limb, the unaffected limb, and CG on a point-by-point basis using a one-way ANOVA for each of the 3 joints of interest, similarly to what was proposed by Bruening et al. . It was Finally, possible differences introduced by OA in gait symmetry parameters were investigated using a MANOVA, considering group (OA/CG) as the independent variable and the 3 symmetry parameters previously listed as dependent variables.A statistical analysis was conducted to evaluate the different gait parameters considered as follows:To compare gait parameters related to the affected and unaffected limbs with respect to CG, a preliminary analysis was performed to exclude the existence of significant differences in the investigated parameters between CG’s left and right limbs. Since such differences were not found, the mean value of each investigated parameter calculated across the two limbs was considered representative of each participant enrolled in the CG.p = 0.05 and the effect sizes were assessed using the eta-squared (η2) coefficient. For each MANOVA , univariate ANOVA was performed as a post-hoc test by reducing the level of significance to p = 0.05/n (where n is the number of the dependent variables) after a Bonferroni adjustment for multiple comparisons. Where necessary, post-hoc Holm–Sidak tests were performed to assess pairwise intra- and inter-group differences. All analyses were performed using the SPSS version 20b software .In all cases, the level of significance was set at The results of the comparison between individuals with OA and unaffected individuals with regard to the spatial-temporal parameters GVS, GPS, dynamic ROM of gait, and symmetry indexes are summarized in Tables 2–5. Graphical representations of the comparison between individuals with OA and unaffected individuals of GPS values, dynamic ROM, and symmetry index together with the angle variations in the sagittal plane during the gait cycle for hip, knee, and ankle joints are reported in Figures 4–8.p < 0.001, Wilks λ = 0.258, η2 = 0.742). The subsequent follow-up ANOVA indicated that individuals with OA are characterized by significantly reduced velocity and cadence (p < 0.001). A significant main effect of the disease was also detected when analyzing stance phase, swing phase, and step length  = 8.205, p < 0.001, Wilks λ = 0.204, η2 = 0.582). In particular, the follow-up ANOVA indicated that individuals with OA exhibited a prolonged stance phase for both the affected and unaffected limbs compared with CG , a reduced swing phase of the unaffected limb (p < 0.001), and a prolonged double support phase duration for both the affected and unaffected limbs compared to CG (p = 0.017 and p = 0.006 respectively). Finally, step length was also found to be significantly reduced in both the affected and unaffected limbs with respect to CG (p < 0.001)   = 17.257, < 0.001) .p = 0.001, Wilks λ = 0.164, η2 = 0.595). The subsequent post-hoc analysis showed that individuals with OA exhibited increased GPS for both the affected and unaffected limbs in comparison to CG and increased GVS for pelvic obliquity and hip flex-extension. A significant increase in GVS was also found for the pelvic rotation, hip ab-adduction, and ankle dorsi-plantarflexion of the unaffected limb  = 3.087, ted limb . Figure p < 0.001, Wilks λ = 0.194, η2 = 0.560). The subsequent follow-up ANOVA indicated that individuals with OA were characterized by significantly reduced dynamic hip, knee, and ankle ROM of the affected limb with respect to CG. Moreover, hip ROM of the affected limb was also reduced when compared to the contralateral hip, while the ROM of the contralateral knee showed a significant reduction with respect to the CG  = 11.871, o the CG .significant differences between the affected and unaffected limbs in individuals with OA at the hip joint from 0 to 17% and from 42 to 68% of the gait cycle, at the knee joint between 56 to 67% and between 81 and 90% of the gait cycle, and at the ankle joint from 56 to 65% of the gait cycle;significant differences between the affected limb of individuals with OA and CG at the hip joint from 25 to 64% of the gait cycle, at the knee joint between 34 and 46% and between 62 and 70% of the gait cycle, and at the ankle joint from 45 to 71% of the gait cycle;significant differences between the unaffected limb of individuals with OA and CG at the hip joint between 5 and 54% of the gait cycle; at the knee joint between 31–37%, 56–72%, and 81–94% of the gait cycle; and at the ankle joint between 45 and 70% of the gait cycle.The analysis of hip, knee, and ankle kinematics in the sagittal plane  revealedp < 0.001, Wilks λ = 0.213, η2 = 0.787, knee F = 3.087, p = 0.001, Wilks λ = 0.414, η2 = 0.586) but not at the ankle joint. In particular, the post-hoc analysis revealed that the cyclogram area was significantly larger in individuals with OA only at the knee joint. In contrast, both cyclogram orientation and Trend Symmetry parameters at the hip joint were found significantly higher in people with OA. Higher Trend Symmetry in people with OA with respect to CG was also detected at the knee joint  = 22.165, oint see . Figure The main aim of the present study was to characterize the main alterations in gait kinematics in individuals affected by hip OA, with a special focus on interlimb asymmetry and detailed point-by-point comparison of the angular trends of hip, knee, and ankle joints between the affected and unaffected limbs of people with OA and with respect to CG. Firstly, it is noteworthy that our results on spatio-temporal parameters are substantially consistent with most previous studies since the gait of individuals with hip OA was found to be characterized by reduced speed, stride length, cadence, and swing phase duration and increased stance and double support phase duration ,25,26. WThe analysis of the quality of gait from a kinematic point of view, performed calculating the summary indexes GPS and GVS, showed a variety of alterations in both limbs which are particularly relevant with regard to hip flexion-extension , ankle dorsi-plantar-flexion, and, to a lesser extent, knee flexion-extension. A relevant deviation from physiological gait was also found in pelvic tilt, rotation, and obliquity. Differences in pelvic tilt may be associated with increased lumbar flexion extension, which has been identified as a method of compensation for the limited range of motion of the hip during gait . The oveAs previously mentioned, one of the novelties of this study is represented by the point-by-point analysis carried out to explore the differences between joint kinematic curves that evolve during the gait cycle. Such an approach allows a better understanding of where gait alterations of the cycle are located, thus providing useful insights mainly in terms of pre- and postoperative exercise training. Firstly, when considering the affected vs. unaffected limb comparison, we observe that at hip level, the differences involve the heel contact and terminal stance/initial swing phases, with the affected hip that exhibits a deficit of flexion in the former period and a deficit of extension in the latter, consistent with what was previously reported by Ismailidis et al.  and ZeniIn contrast, few differences were detected at the knee and ankle joints. In particular, we observed significant changes between the affected and unaffected limbs only during two small periods , likely due to a temporal forward shift of the affected limb curve originated by the reduced duration of the stance phase (62.5% vs. 67.4%). The same cause probably explains the small differences present in the ankle curves at the terminal stance. The comparison between the kinematic curves that refer to the unaffected limb of people with OA and CG revealed the existence of several relevant alterations, probably caused by adaptation strategies created by the presence of the disease. At the hip joint, most of the differences involve the stance phase of the gait cycle, during which people with OA systematically exhibit higher values of flexion. Besides, hip kinematics shows a “hesitation” in flexion-extension motion during stance with respect to the CG. Such findings suggest that people with OA who experience pain, hip flexor contractures, and muscle weakness  tend to The analysis of interlimb joint kinematics symmetry, which was performed using waveform-based methods, shows that individuals with OA exhibit a marked asymmetry at hip and knee joints, but not at the ankle. While the existence of asymmetry at the hip joint is negligible, it is interesting to note the extent of this phenomenon. In fact, both orientation and Trend Symmetry values were found to be 5–6 times higher than those of CG. Just to provide a term of comparison, Goswami et al.  reportedSome limitations of the research are to be acknowledged. Firstly, the limited size of the sample tested here suggests caution when generalizing the result, despite the medium-to-large effect size values obtained from the statistical analysis. The same factor forced us to pool participants of both sexes into a single group, even though previous studies report that gait alterations associated with hip OA differ in men and women with hip OA , thus paIn this study we investigated gait patterns of individuals with hip OA, with a special focus on lower limb joint kinematics. This was done by comparing the joint angle curves of the affected and unaffected limbs and with respect to CG, and by calculating symmetry parameters derived from a waveform-based approach that considers whole curves instead of single discrete parameters. The results show that individuals with hip OA exhibit specific gait patterns which, besides the well-known alterations of spatio-temporal parameters, include severe modifications of the physiologic trend at the hip level even in the unaffected limb , and minor  issues at knee and ankle level. The symmetry analysis also revealed that the effect of the disease on interlimb coordination is present at the knee joint as well as at the hip, while the ankle joint appears relatively exempt from specific negative effects from this point of view. What is the possible role of such analysis from a clinical point of view? Several recent studies pointed out the difficulties encountered in defining the effectiveness of physical therapy carried out before the surgery on gait performance . On the


There is information missing from the Funding statement. The publisher apologizes for the error. The correct Funding statement is as follows: This research project is supported by the Second Century Fund (C2F), Chulalongkorn University, Thailand. This research work is also funded by TSRI Fund (CU_FRB640001_01_21_8). This project is also supported by Taif University Researchers supporting project number (TURSP-2020/228), Taif University, Taif, Saudi Arabia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.