Fuzzy historical STI data and the forecasted results.
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\r\n\t[3] M. Kauranen, A. V. Zayats. Nonlinear Plasmonics. Nature Photonics, vol. 6, 2012, pp. 737-748.
\r\n\t[4] P. Dombi, Z. Pápa, J. Vogelsang et al. Strong-field nano-optics. Reviews of Modern Physics, vol. 92, 2020, pp. 025003-1 – 025003-66.
\r\n\t[5] N. C. Panoiu, W. E. I. Sha, D.Y. Lei, G.-C. Li. Nonlinear optics in plasmonic nanostructures. Journal of Optics, 20, 2018, pp. 1-36.
\r\n\t[6] A. Krasnok, A. Alu. Active nanophotonics. Proceedings of IEEE, vol. 108, 2020, pp. 628-654.
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\r\n\t
In 1965, Zadeh [1] proposed the concept of fuzzy sets as a tool to test the unknown degree of membership. Many fuzzy studies then attempted to use this method as a theoretical framework, which is widely used in the research fields of natural sciences and social sciences, obtaining good study achievements [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]. The fuzzy time series is also an analysis method derived from the concept of fuzzy sets. In 1993, Song and Chissom [18, 19, 20, 21] successfully combined the concept of fuzzy sets with the time series model and began studies on fuzzy time series. Chen [3] proposed the simplified and easy-to-calculate method for Song and Chissom’s model [18, 19, 20, 21], so that the computation complexity of fuzzy time series is dramatically reduced. Lee and Chou [14] also proposed that rational settings of the lower and upper boundary in intervals of the universal set for fuzzy time series have improved their accuracy and reliability. Liaw [17] proposed a simple test method for whether a fuzzy time series has a fuzzy trend, in which the method is used to determine whether the data for analysis is in a steady state. Chou [12] added to Chen and Hsieh’s defuzzification method [2] in the fuzzy time series, so that the long-term level for the series can be obtained, and the model originally used for single-point prediction can be applied to long-term prediction and interval prediction. This article mainly uses the algorithmic method of Chou’s [12] research process for illustrating the fuzzy time series, taking the Taiwan Shipping and Transportation Index (STI) [23] as an example.
\nThe remainder of this chapter is organized as follows. Section 2 presents the definition of fuzzy time series and Section 3 defines the long-term predictive significance level process. A numerical example of STI is shown in Section 4, and concluding remarks are mentioned in conclusion.
\nFuzzy sets, presented by Zadeh [1], have numerous presentations, such as in fuzzy sets, fuzzy decision analysis, and fuzzy time series. The concept is also widely applied in social science article and applications [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]. Fuzzy time series is developed rapidly since their introduction by Song and Chissom [18, 19, 20, 21]. Current fuzzy time series methods have benefited from both theoretical developments as well as relevant applications in research [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], which has led to more diverse uses. This trend indicates that the development of fuzzy time series has markedly improved. The definitions of the fuzzy time series used in this article are described as follows.
\nDefinition 1 [18, 19, 20, 21]. A fuzzy number on the real line \n
Definition 2 [18, 19, 20, 21]. Let \n
Definition 3 [18, 19, 20, 21]. Let \n
Definition 4 [18, 19, 20, 21]. If, for any \n
Definition 5 [18, 19, 20, 21]. Suppose that \n
Definition 6 [18, 19, 20, 21]. Suppose that \n
Definition 7 [18, 19, 20, 21]. Suppose that \n
Definition 8 [3]. \n
Definition 9 [7]. Let \n
\n\n
\n\n
Definition 10 [14]. The universe of discourse \n
Definition 11 [14]. Assuming that there are \n
\n
Definition 12 [17]. For a test \n
Definition 13 [8]. Let \n
Definition 14 [8]. Let \n
\n\n
\n\n
Definition 15 [8]. Let \n
Definition 16 [8]. Let \n
\n\n
\n\n
Definition 17 [8]. The long-term predictive value interval (\n
Definition 18 [2]. Let \n
Definition 19 [12]. Set up new triangular fuzzy numbers by S = (\n
ΔS is called a long-term significance level up, only if: ΔS > \n
ΔS is called a long-term significance level down, only if: ΔS < \n
ΔS is called a long-term significance level stable, only if: ΔS = \n
This section proposes a method to forecast the long-term predictive significance level by Chou. The stepwise procedure of the proposed method consists the following steps [8], illustrated as a flowchart in Figure 1 [5, 6, 7, 8, 9, 10, 11, 12].
\nProcedure of the proposed model.
Step 1. Let \n
Step 2. Determine the universe of discourse \n
Step 3. Define \n
Step 4. Then, \n
Step 5. Derive the transition rule from period \n
Step 6. The value of \n
Step 7. The long-term predictive value interval for \n
Step 8. Set up new triangular fuzzy numbers by ΔS = (\n
Step 9. Defuzzify S to be ΔS.
\nIn this study, the Shipping and Transportation Index (STI) in Taiwan is used for a numerical example. The STI reflects the spot rates of the Taiwan Stock Exchange Corporation. The STI data are sourced from the Taiwan Stock Exchange Corporation [23], the historical data for which is defined here as the STI, and month-averaged data for the period between January, 2015, and June, 2018, was collected.
\nOver these 42 data points, the analysis produces an average of 4.226, with a standard deviation of 0.172, maximum value of 4.571, and minimum value of 4.067. These descriptive statistics show that the STI has largely remained at the 1124.70 level. As shown in Figure 2, its current rate of return is negative.
\nRate of return of the STI.
The following steps in the procedure are performed when using fuzzy time series to analyze STI.
\nStep 1. First, we take the logarithm of the STI data to reduce variation and improve the forecast accuracy, letting \n
Step 2. Maintaining stationary data while forecasting helps to improve the forecast quality; therefore, we conduct a stationary test on the STI data. For fuzzy time series, a fuzzy trend test can measure whether the STI’s fuzzy trend moves upward or downward. Using this fuzzy trend test, the STI data can be converted into a stationary series. If the original STI data exhibited a fuzzy trend, it can be eliminated by taking the difference. We then repeat the test after taking the first difference to measure if the STI data exhibits a fuzzy trend. If a fuzzy trend is again observed, then we take the second difference, and so on.
\nLetting \n
Step 3. According to the interval setting of the STI data, we define the upper and lower bounds, which facilitate dividing the linguistic value intervals later. From Definition 10, the discourse \n
Year | \nActual | \nln(Actual) | \nFuzzified | \nThe forecast value | \n
---|---|---|---|---|
201501 | \n95.611 | \n4.560 | \nA5 | \n4.518 | \n
201502 | \n92.839 | \n4.531 | \nA5 | \n4.518 | \n
201503 | \n94.750 | \n4.551 | \nA5 | \n4.518 | \n
201504 | \n96.622 | \n4.571 | \nA6 | \n4.617 | \n
201505 | \n88.503 | \n4.483 | \nA5 | \n4.518 | \n
201506 | \n79.003 | \n4.369 | \nA4 | \n4.221 | \n
201507 | \n80.103 | \n4.383 | \nA4 | \n4.221 | \n
201508 | \n74.787 | \n4.315 | \nA4 | \n4.221 | \n
201509 | \n69.560 | \n4.242 | \nA4 | \n4.221 | \n
201510 | \n71.416 | \n4.269 | \nA4 | \n4.221 | \n
201511 | \n67.625 | \n4.214 | \nA3 | \n4.221 | \n
201512 | \n64.282 | \n4.163 | \nA3 | \n4.221 | \n
201601 | \n63.301 | \n4.148 | \nA3 | \n4.221 | \n
201602 | \n64.315 | \n4.164 | \nA3 | \n4.221 | \n
201603 | \n67.143 | \n4.207 | \nA3 | \n4.221 | \n
201604 | \n65.073 | \n4.176 | \nA3 | \n4.221 | \n
201605 | \n61.163 | \n4.114 | \nA3 | \n4.221 | \n
201606 | \n61.221 | \n4.114 | \nA3 | \n4.221 | \n
201607 | \n61.043 | \n4.112 | \nA3 | \n4.221 | \n
201608 | \n59.942 | \n4.093 | \nA3 | \n4.221 | \n
201609 | \n60.293 | \n4.099 | \nA3 | \n4.221 | \n
201610 | \n58.372 | \n4.067 | \nA3 | \n4.221 | \n
201611 | \n58.736 | \n4.073 | \nA3 | \n4.221 | \n
201612 | \n57.892 | \n4.059 | \nA3 | \n4.221 | \n
201701 | \n59.278 | \n4.082 | \nA3 | \n4.221 | \n
201702 | \n62.746 | \n4.139 | \nA3 | \n4.221 | \n
201703 | \n65.467 | \n4.182 | \nA3 | \n4.221 | \n
201704 | \n62.142 | \n4.129 | \nA3 | \n4.221 | \n
201705 | \n76.626 | \n4.339 | \nA4 | \n4.221 | \n
201706 | \n63.029 | \n4.144 | \nA3 | \n4.221 | \n
201707 | \n64.728 | \n4.170 | \nA3 | \n4.221 | \n
201708 | \n68.464 | \n4.226 | \nA4 | \n4.221 | \n
201709 | \n70.555 | \n4.256 | \nA4 | \n4.221 | \n
201710 | \n67.830 | \n4.217 | \nA4 | \n4.221 | \n
201711 | \n66.696 | \n4.200 | \nA3 | \n4.221 | \n
201712 | \n67.640 | \n4.214 | \nA3 | \n4.221 | \n
201801 | \n69.769 | \n4.245 | \nA4 | \n4.221 | \n
201802 | \n65.206 | \n4.178 | \nA3 | \n4.221 | \n
201803 | \n64.669 | \n4.169 | \nA3 | \n4.221 | \n
201804 | \n64.671 | \n4.169 | \nA3 | \n4.221 | \n
201805 | \n63.759 | \n4.155 | \nA3 | \n4.221 | \n
201806 | \n62.712 | \n4.139 | \nA3 | \n4.221 | \n
Fuzzy historical STI data and the forecasted results.
Step 4. After defining the upper and lower bounds of the STI data in Step 3, we can define the SCFI range by determining the membership function as well as the linguistic values. We can also define the range of the subinterval for each linguistic value, assuming that the following linguistic values are under consideration: extremely few, very few, few, some, many, very many, and extremely many. According to Definition 11, the supports of fuzzy numbers that represent these linguistic values are given as follows:
\n\n
where \n
Step 5. According to the subinterval setting of each linguistic value, we classified each historical dataset of the STI into its corresponding interval to measure the value corresponding to the linguistic value for each interval. The fuzzy time series \n
Step 6. We apply fuzzy theory to define the corresponding value for the intervals of the STI data, arrange the corresponding method for the STI data, and integrate the changes from all the rules to determine the rules for the STI. The transition rules are derived from Table 1. For example, \n
\n\n | \n
Fuzzy transitions derived from Table 1.
Step 7. We calculate each rule by determining all the rules of the STI, and the calculation results can be used to forecast future values. Table 1 shows the forecasting results from 201001 to 201806.
\nStep 8. The calculated STI rules can define the intervals of the STI data; using these intervals, we can determine the variation in future long-term intervals. The long-term predictive value interval for the STI is given as (3.726, 4.913). Thus, the long-term predictive interval for the STI is given as (41.506, 136.022). Therefore, the current long-term S STI is bounded by this interval. According to Step 8, the fuzzy STI of 201501 shown in Table 1 is A5, and from Table 2, we can see that the rules are the fuzzy logical relationships in Rule 8 of Table 2, in which the current state of fuzzy logical relationships is A3. Thus, the 201806 STI predictive value is 41.506.
\nStep 9. Letting defuzzified S be ΔS, the STI 201806 forecast value based on our investigation is 68.090, and its trading range is between 41.506 and 136.002. Thus, the new triangular fuzzy numbers by S = (41.506, 68.090, 136.002). Thus, the defuzzified S is ΔS = 74.981, and ΔS = 74.981 > \n
The result shows that based on the long-term significance level, the STI is currently oversold. This result and the risk–reward ratio are both related within the group. We used Table 1 data in our analysis according to the root mean square percentage error (R.M.S.P.E.) method, with an average prediction error of 1.708%. Figure 3 shows the forecast visitor arrivals determined through fuzzy time series analysis and the actual STI values. Based on the fuzzy time series results, the average STI is estimated to be 68.090 in 201806 (Figure 3).
\nForecast STI and actual STI.
In this article, a long-term predictive value interval model is developed for forecasting the STI. This model facilitates minimizing the uncertainties associated with fuzzy numbers. The method is examined by forecasting the STI by using data from which ΔS = 74.981 and ΔS > \n
The current model for the STI 201806 forecast level deviates insignificantly from the actual values for an average of 68.090 and is within the group; the prediction error does not exceed 1.708% of the significance level. By constructing a fuzzy time series forecasting model for the STI with an error of less than 1.708%, with the traditional fuzzy time excluded from the single-point forecast comparison, this model provides a long-term predictive significance level.
\nFurthermore, the proposed method can be computerized. Thus, by improving fuzzy linguistic assessments as well as the evaluation of fuzzy time series, decision makers can automatically obtain the final long-term predictive significance level.
\nThe STI used in this chapter is used as a forecasting example. If you predict that the future will rise, you can use the buying strategy. For example, if the index returns in the future, you can use the selling strategy.
\nThe four functions of management are mainly four functions: planning, organization, leadership and control. The fuzzy time series mode used in this chapter can be applied to controlled projects to compare and correct whether the re-executed work meets expectations. If you meet expectations, re-plan the original settings.
\nThis chapter is extended and revised the article “An improved fuzzy time series theory with applications in the Shanghai containerized freight index”.
\nHistoplasmosis is an uncommon endemic mycosis caused by the fungus Histoplasma capsulatum that usually causes an asymptomatic infection but occasionally results in severe multisystem disease [1, 2]. Two main varieties of the saprophytic, thermally dimorphic fungus of the genus Histoplasma affect humans; Histoplasma capsulatum var. capsulatum and Histoplasma capsulatum var. duboisii [3, 4, 5]. Histoplasmosis affects over 10,000 people globally; it is neglected, worryingly under-diagnosed, and often misdiagnosed as cancer or tuberculosis with fatal consequences [6, 7, 8]. The spectrum of the clinical manifestation of histoplasmosis is very broad, ranging from an asymptomatic or minimally symptomatic acute pulmonary disease following inhalation of a large inoculum of Histoplasma microconidia to chronic pulmonary disease in patients with underlying structural lung disease, to acute progressive disseminated disease in patients with severe immunodeficiency [9, 10, 11, 12, 13]. In immunocompetent patients, acute histoplasmosis is typically a self-limiting disease with no need for antifungal therapy [14]. Pneumonia remains the most common disease presentation but extrapulmonary dissemination can occur, especially in immunocompromised patients [12]. A definite diagnosis of histoplasmosis is based on the isolation of the organisms in fungal culture [15]. Rapid detection of the H. capsulatum polysaccharide antigen using enzyme immunoassay (EIA) in urine, blood or bronchoalveolar lavage fluid is also available and very useful especially among immunocompromised patients with disseminated or acute pulmonary disease [13, 16]. Serologic testing for antibodies can be achieved by EIA, immunodiffusion, and complement fixation [13, 16, 17]. These antibody tests may be falsely negative in immunosuppressed patients and are most valuable when combined with a compatible clinical presentation and epidemiologic risk factors.
This chapter provides an overview of the currently available treatment options for the different manifestations of histoplasmosis.
Histoplasmosis is primarily treated with 2 distinct classes of antifungal agents: The triazoles and the polyenes. Data is lacking on the use of the echinocandins for the treatment of histoplasmosis.
The triazole class of antifungals examples of which includes drug like fluconazole, itraconazole, voriconazole, posaconazole and isavuconazole [18, 19]. Of these, itraconazole is the drug of choice for the treatment of the various forms of histoplasmosis as a sole therapy or as a step-down therapy following amphotericin B infusion [13, 14, 20].
Triazole antifungals inhibit the cytochrome P450-dependent enzyme 14-alpha-lanosterol demethylase (CYP51) encoded by the ERG11 gene that converts lanosterol to ergosterol in the cell membrane, inhibiting fungal growth and replication (fungistatic) [18, 19]. Drug-drug and drug-food interactions, as well as side effects are major issues associated with itraconazole therapy. Common side effects of itraconazole include drug-induced hepatitis, gastrointestinal discomfort, heart failure, ankle edema, alopecia, erectile dysfunction, gynaecomastia, peripheral neuropathy, visual disturbance and headaches [21]. Itraconazole interacts with agents used in the treatment of HIV (especially, nevirapine) and tuberculosis (especially, rifampicin) leading to decreased or increased exposures to itraconazole and therefore increased risk of hepatotoxicity. With the exception of fluconazole, voriconazole and posaconazole are used as alternative agents for the treatment of histoplasmosis if there is a contraindication to the use of itraconazole [13, 14, 20]. However, to the best of our knowledge, there is no data to support the use of isavuconazole for the treatment of histoplasmosis.
The polyene antibiotic, amphotericin B derived from the fermentation product of the filamentous bacteria, Streptomyces nodosus is a broad-spectrum antifungal agent administered as deoxycholate (conventional amphotericin B) or liposomal formulation [18, 19].
In its mechanism of action, amphotericin B preferentially binds ergosterol in the fungal cell membrane (and to a lesser extent, cholesterol in cell membrane of humans) to form channels through which small molecules leak from the inside of the fungal cell to the outside, resulting in death of the fungal cell (fungicidal) [18, 19].
Common side effects of amphotericin B include, acute infusion-related reactions characterised by nausea, vomiting, rigors, fever, hyper or hypotension, and hypoxia mainly driven by the effects of amphotericin B on pro-inflammatory cytokine production. Nephrotoxicity is another very important side effect of amphotericin B occurring in about 34–60% of patients. Blood disorders, especially anaemia and electrolyte disturbances, especially hypomagnesaemia and hypokalemia are common but can be part of the rare Fanconi syndrome. Other side effects include infusion site phlebitis, and acute kidney injury [21].
It is important to understand the indications and the choices of antifungal agents in the treatment of histoplasmosis. Manifestations of histoplasmosis that are a typically treated include moderate to severe acute pulmonary histoplasmosis, symptomatic chronic cavitary pulmonary histoplasmosis, acute progressive disseminated disease, and histoplasmosis in immunocompromised individuals [12, 13], (Table 1). Treatment is also indicated for complications of histoplasmosis such as mediastinal granulomas and adenitis [14], (Table 1). Itraconazole can be used for the treatment of symptomatic immunocompetent patients with indolent non-meningeal infection, including chronic cavitary pulmonary histoplasmosis [12, 13]. Itraconazole is also used as a step-down oral agent following initial treatment of severe disease with amphotericin B [12, 13]. Itraconazole is administered at a dose 200 mg 3 times daily for 3 days, as loading dose and then 200 mg once or twice daily, for maintenance therapy [12, 13]. Duration of itraconazole therapy depends on the histoplasmosis syndrome being managed (Table 1).
Histoplasmosis syndrome or complication | Risk factor | Clinical time-course | Indication for treatment | Treatment | Antifungal therapy duration | |
---|---|---|---|---|---|---|
Acute progressive disseminated histoplasmosis | Immunosuppression such as advanced HIV disease | 1–2 weeks | Always | Mild disease | Itraconazole | 12 months followed by maintenance antifungal suppression until immune recovery |
Moderate to severe disease | Amphotericin B for 1–2 weeks in non-meningeal and 4–6 weeks in meningeal disease followed by itraconazole as step-down therapy | |||||
Acute pulmonary histoplasmosis | High inoculum exposure to Histoplasma conidia | 1–2 weeks | Moderate or severe disease or immunosuppressed patient | Mild disease | Itraconazole | 12 weeks |
Severe disease | Amphotericin B for 1–2 weeks followed by itraconazole as step-down therapy | |||||
Subacute pulmonary histoplasmosis | Low inoculum exposure to Histoplasma conidia | Weeks to months | If symptoms last >1 months or immunosuppressed patient | Itraconazole | 6–12 weeks | |
Chronic cavitary pulmonary histoplasmosis | Chronic obstructive pulmonary disease and other lung diseases Smoking | Months to years | Always | Itraconazole | 1–2 years and until radiologic resolution or stabilisation | |
Mediastinal adenitis | Reactive and enlarged mediastinal lymph nodes | Early complication | If compressive symptoms present or adenitis last >1 months | Itraconazole and steroids | 6–12 weeks | |
Mediastinal granuloma | Coalesced necrotic mediastinal lymph nodes | Early or late complication | If compressive symptoms present | Surgery and itraconazole | 6–12 weeks | |
Mediastinal fibrosis | Fibrosis of mediastinal structures | Late complication | If compressive symptoms present | Stenting, arterial embolization or surgery | Not applicable |
Amphotericin by intravenous infusion is the drug of choice for the initial treatment of fulminant or moderate to severe infections, followed by a course of oral. Following successful treatment, itraconazole can be used for secondary prophylaxis against relapse until immune reconstitution is realised [12, 13]. The deoxycholate formulation of amphotericin B is administered at a dose of 0.7–1.0 mg/kg daily by intravenous infusion, meanwhile the lipid formulation of amphotericin B can be administered at a higher dose of 3.0–5.0 mg/kg daily. Liposomal formulation is preferred to the deoxycholate due to its superior side effect profile, better response rate and survival benefit [14, 20].
There are no known prospective studies that have evaluated treatment of central nervous system (CNS) histoplasmosis. Treatment recommendations are guided by several case reports, retrospective case series and expert opinion [12, 13].
Liposomal amphotericin B achieves higher CNS concentrations than deoxycholate formulations and together with triazoles, is the recommended therapy for CNS histoplasmosis [22]. While the choice of the optimal triazole is in doubt, there is evidence from an animal model to suggest that fluconazole may be antagonistic when combined with amphotericin B [23]. Voriconazole could have a role among patients with good performance status as monotherapy or in combination with amphotericin B, but the evidence is limited and the occurrence of hepatotoxicity and hypersensitivity may limit its use [24, 25, 26]. One case series of 11 cases reported a morbidity free survival of 54.5% when patients were treated with intravenous amphotericin B deoxycholate for 8 weeks followed by maintenance therapy with fluconazole or itraconazole for 12–18 months [27].
The Infectious Diseases Society of America (IDSA) recommends treating CNS disease with liposomal amphotericin B (3.0–5.0 mg/kg daily for a total of 175 mg/kg given over 4–6 weeks), followed by a maintenance phase with itraconazole for at least a year at a dose of 200 mg, given 2–3 times a day [14]. Resolution of CSF abnormalities, including a negative antigen test is the recommended treatment target. Specific recommendations for CSF monitoring are: (a) worsening disease in the initial 2 weeks of therapy or lack of improvement by 1 month of therapy, in order to re-evaluate the diagnosis, (b) when amphotericin B is being replaced by a triazole, (c) if relapse is suspected and (d) after 1 year of therapy, to make the decision of treatment continuation or stoppage. Therapeutic drug monitoring for itraconazole is recommended during treatment to ensure adequate drug exposure. Treatment recommendations for histoplasmosis meningitis or CNS masses are the same. Also, for patients with concurrent pulmonary disease, chronic suppressive therapy with 200 mg of itraconazole, given once a day, is indicated until the patient’s immune system is reconstituted [13, 14, 28].
The role of steroids is not well described, although case reports indicate they can be successfully used [14] . Similarly, routine brain or spinal cord surgery is not recommended by IDSA but is chronicled in case reports [14, 28]. Hydrocephalus complicating CNS histoplasmosis may be managed with shunt placement when the patient has received at least 2 weeks of amphotericin B therapy [22]. The management of increased intracranial pressure and the long-term sequelae that require rehabilitation is similar to what is done for stroke and brain tumours [14, 28].
In a sizable cohort of patients, the one-year survival of patients with CNS histoplasmosis was reported to be 75% among patients who were initially treated with liposomal or deoxycholate formulations of amphotericin [28].
All presenting clinical syndromes of histoplasmosis in pregnancy require antifungal therapy due to the increased risk of trans-placental transmission of the infection to the developing fetus [29]. Preferably, pregnant women can be treated with amphotericin B preparations for 4–6 weeks. Lipid formulation of amphotericin B is given at a dose of 3–5 mg/kg/day. For pregnant women with low risk for nephrotoxicity, amphotericin B deoxycholate (0.7–1.0 mg/kg/day) may be offered as a substitute [14, 30]. Coupled with this, post-partum monitoring of the child for any evidence of the infection is vital. In any case of the newborn showing signs of histoplasmosis infection, it is recommended that treatment of amphotericin B deoxycholate (1 mg/kg/day) is given for 4 weeks [14, 30].
Itraconazole is generally considered teratogenic to the growing fetus and is therefore best avoided in pregnancy. However, itraconazole may be considered for treatment in pregnant women with systemic histoplasmosis, but only after the first trimester [31, 32]. Amphotericin B therefore remains the drug of choice in managing histoplasmosis in pregnancy, as azole antifungals should generally be avoided.
Immune reconstitution inflammatory syndrome (IRIS) is a spectrum of inflammatory disorders linked with paradoxical worsening of pre-existing infectious diseases (previously diagnosed or subclinical) in HIV-infected patients. It typically follows, the initiation of antiretroviral therapy (ART): whereby the ART improves the patient’s immune system enough to mount an inflammatory response tends to unmask the underlying infectious processes, such as histoplasmosis [33, 34, 35, 36].
In studies done among HIV patients, histoplasmosis-associated - IRIS was associated with histoplasmosis; IRIS was reported to be uncommon with incidence rates of 0.74 cases per 1000 HIV-infected person-years 0.5% [37, 38, 39]. However according to another study among 271 patients, the emergence of IRIS tended to be quite common in people with HIV and disseminated histoplasmosis; whether the IRIS is triggered by the Histoplasma or other co-infections, is still unclear [40].
For HIV patients with histoplasmosis to be considered to have histoplasmosis -associated IRIS, they should fulfil most or all of the following criteria: AIDS with low pretreatment CD4 count (≤100 cells/μL), a positive immunological and virological response to ART, clinical manifestations of an inflammatory condition, association between ART initiation and appearance of clinical features of the inflammatory condition and; absence of evidence of ART resistance, patient non-compliance, drug allergy or adverse reactions, a concomitant non- fungal infection, or decreased drug levels due to malabsorption or drug-drug interactions [36, 38, 41, 42, 43].
The clinical manifestations vary from case to case, and they commonly include fever, lymphadenopathy, mucocutaneous lesions, and disseminated disease. The timeline of occurrence from initiation of ART is also varied from a few days to months, and symptoms can set in when the patient is already on the ART course or when the ART is just introduced [39, 44, 45]. Histoplasmosis-associated IRIS in HIV patients may have a predilection for females over males, with one study showing that it was four times more frequent in females than males [39].
If the symptoms of IRIS are mild, the patient is managed symptomatically. The role of steroids in treating histoplasmosis-IRIS is yet unclear [39].
However if the presentation is severe, corticosteroids may be used as they have been seen to be of benefit in TB-associated IRIS, though they should be used with caution [46].
Once a patient exhibits features of IRIS while already on effective ART, the ART should be continued and the patient should be initiated on histoplasmosis treatment immediately. However if the patient is not receiving ART, a two-week delay is encouraged before starting ART while the patient is on antifungal therapy (amphotericin B or itraconazole) as per the guidelines [14].
Itraconazole alone is used for the treatment of mild forms of histoplasmosis and as a step-down therapy in severe disease and for secondary prophylaxis to prevent relapse in the immunocompromised after induction therapy with amphotericin B. Moderate-to-severe acute pulmonary histoplasmosis as well as acute progressive disseminated histoplasmosis require intravenous amphotericin B therapy for at least 2 weeks (4–6 weeks if meningeal involvement) or until a patient can tolerate oral therapy, then oral itraconazole (or an alternative triazole) for at least 12 weeks (for acute pulmonary) or 12 months (for acute progressive disseminated histoplasmosis). Chronic cavitary pulmonary histoplasmosis is treated with itraconazole for 12–24 months. There is insufficient evidence to support the use of isavuconazole and the echinocandins for the treatment of histoplasmosis.
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\\n\\nIntechOpen has collaborated with Enago, through its sister brand, Ulatus, which is one of the world’s leading providers of book translation services. The services are designed to convey the essence of your work to readers from across the globe in a language they understand. Enago’s expert translators incorporate cultural nuances in translations to make the content relevant for local audiences while retaining the original meaning and style. Enago translators are equipped to handle all complex and multiple overlapping themes encompassed in a single book and their high degree of linguistic and subject expertise enables them to deliver a superior quality output.
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\n\n*The price does not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate applied in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT by providing us with their VAT registration number. This is made possible by the EU reverse charge method.
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\n\nIntechOpen Authors that wish to use this service will receive a 20% discount on all translation services. To find out more information or obtain a quote, please visit: https://www.enago.com/intech.
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\n\nWe feel that financial barriers should never prevent researchers from publishing their work. Please consult our Open Access Funding page to explore funding opportunities and learn more about how you can finance your IntechOpen publication.
\n\nBENEFITS
\n\nPUBLISHING PROCESS STEPS
\n\nFor a complete overview of all publishing process steps and descriptions, go to How Open Access Publishing Works.
\n\nSEND YOUR PROPOSAL
\n\nIf you are interested in publishing your book with IntechOpen, please submit your book proposal by completing the Publishing Proposal Form.
\n\nNot sure if this is the right option for you? Please refer back to the main Publish with IntechOpen page or feel free to contact us directly at book.department@intechopen.com.
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